Processing audio and video

ABSTRACT

A wearable device may include an image sensor configured to capture a plurality of images from an environment, a microphone configured to capture sounds from the environment, and at least one processor. The at least one processor may be programmed to receive audio signals representative of the sounds captured by the at least one microphone, and receive a first image including a representation of a first individual from among the plurality of images captured by the image sensor. The at least one processor may also be programmed to obtain a first audio segment from the audio signals using the first image. The first audio segment may include a first portion of the audio signals in which the first individual is speaking. The at least one processor may also be programmed to receive a second image including a representation of a second individual from among the plurality of images captured by the image sensor, and obtain a second audio segment from the audio signals using the second image. The second audio segment may include a second portion of the audio signals in which the second individual is speaking. The at least one processor may also be programmed to receive a third image including a representation of the first individual from among the plurality of images captured by the image sensor, and using the third image, obtain a third audio segment from the audio signals. The audio segment may include a third portion of the audio signals in which the first individual is speaking. The at least one processor may also associate the first and third audio segments with the first individual and associate the second audio segment with the second individual.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to and the benefit of U.S. ProvisionalApplication No. 63/015,709, filed Apr. 27, 2020, which is herebyincorporated by reference in its entirety.

BACKGROUND Technical Field

This disclosure generally relates to devices and methods for capturingand processing images and audio from an environment of a user, and usinginformation derived from captured images and audio.

Background Information

Today, technological advancements make it possible for wearable devicesto automatically capture images and audio, and store information that isassociated with the captured images and audio. Certain devices have beenused to digitally record aspects and personal experiences of one's lifein an exercise typically called “lifelogging.” Some individuals logtheir life so they can retrieve moments from past activities, forexample, social events, trips, etc. Lifelogging may also havesignificant benefits in other fields (e.g., business, fitness andhealthcare, and social research). Lifelogging devices, while useful fortracking daily activities, may be improved with capability to enhanceone's interaction in his environment with feedback and other advancedfunctionality based on the analysis of captured image and audio data.

Even though users can capture images and audio with their smartphonesand some smartphone applications can process the captured information,smartphones may not be the best platform for serving as lifeloggingapparatuses in view of their size and design. Lifelogging apparatusesshould be small and light, so they can be easily worn. Moreover, withimprovements in image capture devices, including wearable apparatuses,additional functionality may be provided to assist users in navigatingin and around an environment, identifying persons and objects theyencounter, and providing feedback to the users about their surroundingsand activities. Therefore, there is a need for apparatuses and methodsfor automatically capturing and processing images and audio to provideuseful information to users of the apparatuses, and for systems andmethods to process and leverage information gathered by the apparatuses.

SUMMARY

Embodiments consistent with the present disclosure provide devices andmethods for automatically capturing and processing images and audio froman environment of a user, and systems and methods for processinginformation related to images and audio captured from the environment ofthe user.

In an embodiment, a wearable device for processing audio signals mayinclude an image sensor configured to capture a plurality of images froman environment, a microphone configured to capture sounds from theenvironment, and at least one processor. The at least one processor maybe programmed to receive audio signals representative of the soundscaptured by the at least one microphone, and receive a first imageincluding a representation of a first individual from among theplurality of images captured by the image sensor. The at least oneprocessor may also be programmed to obtain a first audio segment fromthe audio signals using the first image. The first audio segment mayinclude a first portion of the audio signals in which the firstindividual is speaking. The at least one processor may also beprogrammed to receive a second image including a representation of asecond individual from among the plurality of images captured by theimage sensor, and obtain a second audio segment from the audio signalsusing the second image. The second audio segment may include a secondportion of the audio signals in which the second individual is speaking.The at least one processor may also be programmed to receive a thirdimage including a representation of the first individual from among theplurality of images captured by the image sensor, and using the thirdimage, obtain a third audio segment from the audio signals. The audiosegment may include a third portion of the audio signals in which thefirst individual is speaking. The at least one processor may furtherassociate the first and third audio segments with the first individualand associate the second audio segment with the second individual.

In an embodiment, a wearable device for processing audio signals mayinclude an image sensor configured to capture a plurality of images froman environment, a microphone configured to capture sounds from theenvironment, and at least one processor. The at least one processor maybe programmed to receive audio signals representative of the soundscaptured by the at least one microphone, receive a first image fromamong the plurality of images captured by the image sensor. The firstimage may include a first representation of a first individual. The atleast one processor may also be programmed to obtain a first audiosegment which includes a first portion of the audio signals in which thefirst individual is speaking from the audio signals, extract a firstvoice print of the first individual from the first audio segment, andstore the first voice print of the first individual in a database inassociation with the first image. The at least one processor may also beprogrammed to receive a second image from among the plurality of imagescaptured by the image sensor. The second image may include a secondrepresentation of a second individual. The at least one processor mayalso be programmed to obtain a second audio segment from the audiosignals. The second audio segment may include a second portion of theaudio signals in which the second individual is speaking. The firstaudio segment and the second audio segment may be non-overlapping. Theat least one processor may further be programmed to extract a secondvoice print of the second individual from the second audio segment,store the second voice print of the second individual in the database inassociation with the second image.

In an embodiment, a wearable device for processing audio signals mayinclude an image sensor configured to capture a plurality of images froman environment, a microphone configured to capture sounds from theenvironment, and at least one processor. The at least one processor maybe programmed to receive first audio signals representative of thesounds captured by the at least one microphone during a first timeperiod, receive a first image including a representation of anindividual from among the plurality of images captured by the imagesensor, obtain a first audio segment which includes a portion of thefirst audio signals in which the individual is speaking from the firstaudio signals, extract a voice print of the individual from the firstaudio segment, and store the voice print of the individual in a databasein association with the first image. The at least one processor may alsobe programmed to receive second audio signals representative of thesounds captured by the at least one microphone during a second timeperiod later than the first time period, receive a second imageincluding a representation of a person from among the plurality ofimages captured by the image sensor, obtain a second audio segment whichincludes a portion of the second audio signals in which the person isspeaking from the second audio signals, extract a voice print of theperson from the second audio segment. The at least one processor mayfurther be programmed to determine that the person is the individual byat least one of (a) comparing the extracted voice print of the person toone or more voice prints stored in the database, or (b) comparing thesecond image with one or more images stored in the database.

In an embodiment, a method of processing audio signals may includereceiving audio signals representative of the sounds captured by the atleast one microphone of a wearable device, receiving a first imageincluding a representation of a first individual from among theplurality of images captured by an image sensor of the wearable device,obtaining a first audio segment from the audio signals using the firstimage. The first audio segment may include a portion of the audiosignals in which the first individual is speaking. The method may alsoinclude receiving a second image including a representation of a secondindividual from among the plurality of images captured by the imagesensor, the second image, and obtaining a second audio segment from theaudio signals using the second image. The second audio segment mayinclude a portion of the audio signals in which the second individual isspeaking. The method may also include receiving a third image includinga representation of the first individual from among the plurality ofimages captured by the image sensor, and using the third image,obtaining a third audio segment from the audio signals. The audiosegment may include a third portion of the audio signals in which thefirst individual is speaking. The method may include associating thefirst and third audio segments with the first individual and associatingthe second audio segment with the second individual.

Consistent with other disclosed embodiments, non-transitory computerreadable storage media may store program instructions, which areexecuted by at least one processor and perform any of the methodsdescribed herein.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various disclosed embodiments. Inthe drawings:

FIG. 1A is a schematic illustration of an example of a user wearing awearable apparatus according to a disclosed embodiment.

FIG. 1B is a schematic illustration of an example of the user wearing awearable apparatus according to a disclosed embodiment.

FIG. 1C is a schematic illustration of an example of the user wearing awearable apparatus according to a disclosed embodiment.

FIG. 1D is a schematic illustration of an example of the user wearing awearable apparatus according to a disclosed embodiment.

FIG. 2 is a schematic illustration of an example system consistent withthe disclosed embodiments.

FIG. 3A is a schematic illustration of an example of the wearableapparatus shown in FIG. 1A.

FIG. 3B is an exploded view of the example of the wearable apparatusshown in FIG. 3A.

FIG. 4A-4K are schematic illustrations of an example of the wearableapparatus shown in FIG. 1B from various viewpoints.

FIG. 5A is a block diagram illustrating an example of the components ofa wearable apparatus according to a first embodiment.

FIG. 5B is a block diagram illustrating an example of the components ofa wearable apparatus according to a second embodiment.

FIG. 5C is a block diagram illustrating an example of the components ofa wearable apparatus according to a third embodiment.

FIG. 6 illustrates an exemplary embodiment of a memory containingsoftware modules consistent with the present disclosure.

FIG. 7 is a schematic illustration of an embodiment of a wearableapparatus including an orientable image capture unit.

FIG. 8 is a schematic illustration of an embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure.

FIG. 9 is a schematic illustration of a user wearing a wearableapparatus consistent with an embodiment of the present disclosure.

FIG. 10 is a schematic illustration of an embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure.

FIG. 11 is a schematic illustration of an embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure.

FIG. 12 is a schematic illustration of an embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure.

FIG. 13 is a schematic illustration of an embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure.

FIG. 14 is a schematic illustration of an embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure.

FIG. 15 is a schematic illustration of an embodiment of a wearableapparatus power unit including a power source.

FIG. 16 is a schematic illustration of an exemplary embodiment of awearable apparatus including protective circuitry.

FIG. 17A is a block diagram illustrating components of a wearableapparatus according to an example embodiment.

FIG. 17B is a block diagram illustrating the components of a wearableapparatus according to another example embodiment.

FIG. 17C is a block diagram illustrating the components of a wearableapparatus according to another example embodiment.

FIG. 18 is a schematic illustration of an example of a user wearing anapparatus for a camera-based hearing aid device according to a disclosedembodiment.

FIG. 19 is a schematic illustration of an embodiment of an apparatussecurable to an article of clothing consistent with the presentdisclosure.

FIG. 20 is a schematic illustration showing an exemplary environment foruse of a camera-based hearing aid consistent with the presentdisclosure.

FIG. 21 is a flowchart showing an exemplary process for selectivelyamplifying sounds emanating from a detected look direction of a userconsistent with disclosed embodiments.

FIG. 22 is a schematic illustration showing an exemplary environment foruse of a hearing aid with voice and/or image recognition consistent withthe present disclosure.

FIG. 23 illustrates an exemplary embodiment of an apparatus comprisingfacial and voice recognition components consistent with the presentdisclosure.

FIG. 24 is a flowchart showing an exemplary process for selectivelyamplifying audio signals associated with a voice of a recognizedindividual consistent with disclosed embodiments.

FIG. 25 is a flowchart showing an exemplary process for selectivelytransmitting audio signals associated with a voice of a recognized userconsistent with disclosed embodiments.

FIG. 26 is a schematic illustration showing an exemplary individual thatmay be identified in the environment of a user consistent with thepresent disclosure.

FIG. 27 is a schematic illustration showing an exemplary individual thatmay be identified in the environment of a user consistent with thepresent disclosure.

FIG. 28 illustrates an exemplary lip-tracking system consistent with thedisclosed embodiments.

FIG. 29 is a schematic illustration showing an exemplary environment foruse of a lip-tracking hearing aid consistent with the presentdisclosure.

FIG. 30 is a flowchart showing an exemplary process for selectivelyamplifying audio signals based on tracked lip movements consistent withdisclosed embodiments.

FIG. 31A is a schematic illustration of diarization of audio signalsusing images.

FIG. 31B is an illustration of an exemplary database used in associationwith a wearable apparatus of the current disclosure in some embodiments.

FIGS. 32A and 32B are flowchart of exemplary methods for processingaudio and video, consistent with disclosed embodiments.

FIGS. 33, 34, and 35 are flowcharts of other exemplary method forprocessing audio and video, consistent with disclosed embodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions or modifications may be made to thecomponents illustrated in the drawings, and the illustrative methodsdescribed herein may be modified by substituting, reordering, removing,or adding steps to the disclosed methods. Accordingly, the followingdetailed description is not limited to the disclosed embodiments andexamples. Instead, the proper scope is defined by the appended claims.

FIG. 1A illustrates a user 100 wearing an apparatus 110 that isphysically connected (or integral) to glasses 130, consistent with thedisclosed embodiments. Glasses 130 may be prescription glasses,magnifying glasses, non-prescription glasses, safety glasses,sunglasses, etc. Additionally, in some embodiments, glasses 130 mayinclude parts of a frame and earpieces, nosepieces, etc., and one or nolenses. Thus, in some embodiments, glasses 130 may function primarily tosupport apparatus 110, and/or an augmented reality display device orother optical display device. In some embodiments, apparatus 110 mayinclude an image sensor (not shown in FIG. 1A) for capturing real-timeimage data of the field-of-view of user 100. The term “image data”includes any form of data retrieved from optical signals in thenear-infrared, infrared, visible, and ultraviolet spectrums. The imagedata may include video clips and/or photographs.

In some embodiments, apparatus 110 may communicate wirelessly or via awire with a computing device 120. In some embodiments, computing device120 may include, for example, a smartphone, or a tablet, or a dedicatedprocessing unit, which may be portable (e.g., can be carried in a pocketof user 100). Although shown in FIG. 1A as an external device, in someembodiments, computing device 120 may be provided as part of wearableapparatus 110 or glasses 130, whether integral thereto or mountedthereon. In some embodiments, computing device 120 may be included in anaugmented reality display device or optical head mounted displayprovided integrally or mounted to glasses 130. In other embodiments,computing device 120 may be provided as part of another wearable orportable apparatus of user 100 including a wrist-strap, amultifunctional watch, a button, a clip-on, etc. And in otherembodiments, computing device 120 may be provided as part of anothersystem, such as an on-board automobile computing or navigation system. Aperson skilled in the art can appreciate that different types ofcomputing devices and arrangements of devices may implement thefunctionality of the disclosed embodiments. Accordingly, in otherimplementations, computing device 120 may include a Personal Computer(PC), laptop, an Internet server, etc.

FIG. 1B illustrates user 100 wearing apparatus 110 that is physicallyconnected to a necklace 140, consistent with a disclosed embodiment.Such a configuration of apparatus 110 may be suitable for users that donot wear glasses some or all of the time. In this embodiment, user 100can easily wear apparatus 110, and take it off.

FIG. 1C illustrates user 100 wearing apparatus 110 that is physicallyconnected to a belt 150, consistent with a disclosed embodiment. Such aconfiguration of apparatus 110 may be designed as a belt buckle.Alternatively, apparatus 110 may include a clip for attaching to variousclothing articles, such as belt 150, or a vest, a pocket, a collar, acap or hat or other portion of a clothing article.

FIG. 1D illustrates user 100 wearing apparatus 110 that is physicallyconnected to a wrist strap 160, consistent with a disclosed embodiment.Although the aiming direction of apparatus 110, according to thisembodiment, may not match the field-of-view of user 100, apparatus 110may include the ability to identify a hand-related trigger based on thetracked eye movement of a user 100 indicating that user 100 is lookingin the direction of the wrist strap 160. Wrist strap 160 may alsoinclude an accelerometer, a gyroscope, or other sensor for determiningmovement or orientation of a user's 100 hand for identifying ahand-related trigger.

FIG. 2 is a schematic illustration of an exemplary system 200 includinga wearable apparatus 110, worn by user 100, and an optional computingdevice 120 and/or a server 250 capable of communicating with apparatus110 via a network 240, consistent with disclosed embodiments. In someembodiments, apparatus 110 may capture and analyze image data, identifya hand-related trigger present in the image data, and perform an actionand/or provide feedback to a user 100, based at least in part on theidentification of the hand-related trigger. In some embodiments,optional computing device 120 and/or server 250 may provide additionalfunctionality to enhance interactions of user 100 with his or herenvironment, as described in greater detail below.

According to the disclosed embodiments, apparatus 110 may include animage sensor system 220 for capturing real-time image data of thefield-of-view of user 100. In some embodiments, apparatus 110 may alsoinclude a processing unit 210 for controlling and performing thedisclosed functionality of apparatus 110, such as to control the captureof image data, analyze the image data, and perform an action and/oroutput a feedback based on a hand-related trigger identified in theimage data. According to the disclosed embodiments, a hand-relatedtrigger may include a gesture performed by user 100 involving a portionof a hand of user 100. Further, consistent with some embodiments, ahand-related trigger may include a wrist-related trigger. Additionally,in some embodiments, apparatus 110 may include a feedback outputtingunit 230 for producing an output of information to user 100.

As discussed above, apparatus 110 may include an image sensor 220 forcapturing image data. The term “image sensor” refers to a device capableof detecting and converting optical signals in the near-infrared,infrared, visible, and ultraviolet spectrums into electrical signals.The electrical signals may be used to form an image or a video stream(i.e., image data) based on the detected signal. The term “image data”includes any form of data retrieved from optical signals in thenear-infrared, infrared, visible, and ultraviolet spectrums. Examples ofimage sensors may include semiconductor charge-coupled devices (CCD),active pixel sensors in complementary metal-oxide-semiconductor (CMOS),or N-type metal-oxide-semiconductor (NMOS, Live MOS). In some cases,image sensor 220 may be part of a camera included in apparatus 110.

Apparatus 110 may also include a processor 210 for controlling imagesensor 220 to capture image data and for analyzing the image dataaccording to the disclosed embodiments. As discussed in further detailbelow with respect to FIG. 5A, processor 210 may include a “processingdevice” for performing logic operations on one or more inputs of imagedata and other data according to stored or accessible softwareinstructions providing desired functionality. In some embodiments,processor 210 may also control feedback outputting unit 230 to providefeedback to user 100 including information based on the analyzed imagedata and the stored software instructions. As the term is used herein, a“processing device” may access memory where executable instructions arestored or, in some embodiments, a “processing device” itself may includeexecutable instructions (e.g., stored in memory included in theprocessing device).

In some embodiments, the information or feedback information provided touser 100 may include time information. The time information may includeany information related to a current time of day and as describedfurther below, may be presented in any sensory perceptive manner. Insome embodiments, time information may include a current time of day ina preconfigured format (e.g., 2:30 pm or 14:30). Time information mayinclude the time in the user's current time zone (e.g., based on adetermined location of user 100), as well as an indication of the timezone and/or a time of day in another desired location. In someembodiments, time information may include a number of hours or minutesrelative to one or more predetermined times of day. For example, in someembodiments, time information may include an indication that three hoursand fifteen minutes remain until a particular hour (e.g., until 6:00pm), or some other predetermined time. Time information may also includea duration of time passed since the beginning of a particular activity,such as the start of a meeting or the start of a jog, or any otheractivity. In some embodiments, the activity may be determined based onanalyzed image data. In other embodiments, time information may alsoinclude additional information related to a current time and one or moreother routine, periodic, or scheduled events. For example, timeinformation may include an indication of the number of minutes remaininguntil the next scheduled event, as may be determined from a calendarfunction or other information retrieved from computing device 120 orserver 250, as discussed in further detail below.

Feedback outputting unit 230 may include one or more feedback systemsfor providing the output of information to user 100. In the disclosedembodiments, the audible or visual feedback may be provided via any typeof connected audible or visual system or both. Feedback of informationaccording to the disclosed embodiments may include audible feedback touser 100 (e.g., using a Bluetooth™ or other wired or wirelesslyconnected speaker, or a bone conduction headphone). Feedback outputtingunit 230 of some embodiments may additionally or alternatively produce avisible output of information to user 100, for example, as part of anaugmented reality display projected onto a lens of glasses 130 orprovided via a separate heads up display in communication with apparatus110, such as a display 260 provided as part of computing device 120,which may include an onboard automobile heads up display, an augmentedreality device, a virtual reality device, a smartphone, PC, table, etc.

The term “computing device” refers to a device including a processingunit and having computing capabilities. Some examples of computingdevice 120 include a PC, laptop, tablet, or other computing systems suchas an on-board computing system of an automobile, for example, eachconfigured to communicate directly with apparatus 110 or server 250 overnetwork 240. Another example of computing device 120 includes asmartphone having a display 260. In some embodiments, computing device120 may be a computing system configured particularly for apparatus 110,and may be provided integral to apparatus 110 or tethered thereto.Apparatus 110 can also connect to computing device 120 over network 240via any known wireless standard (e.g., Wi-Fi, Bluetooth®, etc.), as wellas near-filed capacitive coupling, and other short range wirelesstechniques, or via a wired connection. In an embodiment in whichcomputing device 120 is a smartphone, computing device 120 may have adedicated application installed therein. For example, user 100 may viewon display 260 data (e.g., images, video clips, extracted information,feedback information, etc.) that originate from or are triggered byapparatus 110. In addition, user 100 may select part of the data forstorage in server 250.

Network 240 may be a shared, public, or private network, may encompass awide area or local area, and may be implemented through any suitablecombination of wired and/or wireless communication networks. Network 240may further comprise an intranet or the Internet. In some embodiments,network 240 may include short range or near-field wireless communicationsystems for enabling communication between apparatus 110 and computingdevice 120 provided in close proximity to each other, such as on or neara user's person, for example. Apparatus 110 may establish a connectionto network 240 autonomously, for example, using a wireless module (e.g.,Wi-Fi, cellular). In some embodiments, apparatus 110 may use thewireless module when being connected to an external power source, toprolong battery life. Further, communication between apparatus 110 andserver 250 may be accomplished through any suitable communicationchannels, such as, for example, a telephone network, an extranet, anintranet, the Internet, satellite communications, off-linecommunications, wireless communications, transponder communications, alocal area network (LAN), a wide area network (WAN), and a virtualprivate network (VPN).

As shown in FIG. 2 , apparatus 110 may transfer or receive data to/fromserver 250 via network 240. In the disclosed embodiments, the data beingreceived from server 250 and/or computing device 120 may includenumerous different types of information based on the analyzed imagedata, including information related to a commercial product, or aperson's identity, an identified landmark, and any other informationcapable of being stored in or accessed by server 250. In someembodiments, data may be received and transferred via computing device120. Server 250 and/or computing device 120 may retrieve informationfrom different data sources (e.g., a user specific database or a user'ssocial network account or other account, the Internet, and other managedor accessible databases) and provide information to apparatus 110related to the analyzed image data and a recognized trigger according tothe disclosed embodiments. In some embodiments, calendar-relatedinformation retrieved from the different data sources may be analyzed toprovide certain time information or a time-based context for providingcertain information based on the analyzed image data.

An example of wearable apparatus 110 incorporated with glasses 130according to some embodiments (as discussed in connection with FIG. 1A)is shown in greater detail in FIG. 3A. In some embodiments, apparatus110 may be associated with a structure (not shown in FIG. 3A) thatenables easy detaching and reattaching of apparatus 110 to glasses 130.In some embodiments, when apparatus 110 attaches to glasses 130, imagesensor 220 acquires a set aiming direction without the need fordirectional calibration. The set aiming direction of image sensor 220may substantially coincide with the field-of-view of user 100. Forexample, a camera associated with image sensor 220 may be installedwithin apparatus 110 in a predetermined angle in a position facingslightly downwards (e.g., 5-15 degrees from the horizon). Accordingly,the set aiming direction of image sensor 220 may substantially match thefield-of-view of user 100.

FIG. 3B is an exploded view of the components of the embodimentdiscussed regarding FIG. 3A. Attaching apparatus 110 to glasses 130 maytake place in the following way. Initially, a support 310 may be mountedon glasses 130 using a screw 320, in the side of support 310. Then,apparatus 110 may be clipped on support 310 such that it is aligned withthe field-of-view of user 100. The term “support” includes any device orstructure that enables detaching and reattaching of a device including acamera to a pair of glasses or to another object (e.g., a helmet).Support 310 may be made from plastic (e.g., polycarbonate), metal (e.g.,aluminum), or a combination of plastic and metal (e.g., carbon fibergraphite). Support 310 may be mounted on any kind of glasses (e.g.,eyeglasses, sunglasses, 3D glasses, safety glasses, etc.) using screws,bolts, snaps, or any fastening means used in the art.

In some embodiments, support 310 may include a quick release mechanismfor disengaging and reengaging apparatus 110. For example, support 310and apparatus 110 may include magnetic elements. As an alternativeexample, support 310 may include a male latch member and apparatus 110may include a female receptacle. In other embodiments, support 310 canbe an integral part of a pair of glasses, or sold separately andinstalled by an optometrist. For example, support 310 may be configuredfor mounting on the arms of glasses 130 near the frame front, but beforethe hinge. Alternatively, support 310 may be configured for mounting onthe bridge of glasses 130.

In some embodiments, apparatus 110 may be provided as part of a glassesframe 130, with or without lenses. Additionally, in some embodiments,apparatus 110 may be configured to provide an augmented reality displayprojected onto a lens of glasses 130 (if provided), or alternatively,may include a display for projecting time information, for example,according to the disclosed embodiments. Apparatus 110 may include theadditional display or alternatively, may be in communication with aseparately provided display system that may or may not be attached toglasses 130.

In some embodiments, apparatus 110 may be implemented in a form otherthan wearable glasses, as described above with respect to FIGS. 1B-1D,for example. FIG. 4A is a schematic illustration of an example of anadditional embodiment of apparatus 110 from a front viewpoint ofapparatus 110. Apparatus 110 includes an image sensor 220, a clip (notshown), a function button (not shown) and a hanging ring 410 forattaching apparatus 110 to, for example, necklace 140, as shown in FIG.1B. When apparatus 110 hangs on necklace 140, the aiming direction ofimage sensor 220 may not fully coincide with the field-of-view of user100, but the aiming direction would still correlate with thefield-of-view of user 100.

FIG. 4B is a schematic illustration of the example of a secondembodiment of apparatus 110, from a side orientation of apparatus 110.In addition to hanging ring 410, as shown in FIG. 4B, apparatus 110 mayfurther include a clip 420. User 100 can use clip 420 to attachapparatus 110 to a shirt or belt 150, as illustrated in FIG. 1C. Clip420 may provide an easy mechanism for disengaging and re-engagingapparatus 110 from different articles of clothing. In other embodiments,apparatus 110 may include a female receptacle for connecting with a malelatch of a car mount or universal stand.

In some embodiments, apparatus 110 includes a function button 430 forenabling user 100 to provide input to apparatus 110. Function button 430may accept different types of tactile input (e.g., a tap, a click, adouble-click, a long press, a right-to-left slide, a left-to-rightslide). In some embodiments, each type of input may be associated with adifferent action. For example, a tap may be associated with the functionof taking a picture, while a right-to-left slide may be associated withthe function of recording a video.

Apparatus 110 may be attached to an article of clothing (e.g., a shirt,a belt, pants, etc.), of user 100 at an edge of the clothing using aclip 431 as shown in FIG. 4C. For example, the body of apparatus 100 mayreside adjacent to the inside surface of the clothing with clip 431engaging with the outside surface of the clothing. In such anembodiment, as shown in FIG. 4C, the image sensor 220 (e.g., a camerafor visible light) may be protruding beyond the edge of the clothing.Alternatively, clip 431 may be engaging with the inside surface of theclothing with the body of apparatus 110 being adjacent to the outside ofthe clothing. In various embodiments, the clothing may be positionedbetween clip 431 and the body of apparatus 110.

An example embodiment of apparatus 110 is shown in FIG. 4D. Apparatus110 includes clip 431 which may include points (e.g., 432A and 432B) inclose proximity to a front surface 434 of a body 435 of apparatus 110.In an example embodiment, the distance between points 432A, 432B andfront surface 434 may be less than a typical thickness of a fabric ofthe clothing of user 100. For example, the distance between points 432A,432B and surface 434 may be less than a thickness of a tee-shirt, e.g.,less than a millimeter, less than 2 millimeters, less than 3millimeters, etc., or, in some cases, points 432A, 432B of clip 431 maytouch surface 434. In various embodiments, clip 431 may include a point433 that does not touch surface 434, allowing the clothing to beinserted between clip 431 and surface 434.

FIG. 4D shows schematically different views of apparatus 110 defined asa front view (F-view), a rearview (R-view), a top view (T-view), a sideview (S-view) and a bottom view (B-view). These views will be referredto when describing apparatus 110 in subsequent figures. FIG. 4D shows anexample embodiment where clip 431 is positioned at the same side ofapparatus 110 as sensor 220 (e.g., the front side of apparatus 110).Alternatively, clip 431 may be positioned at an opposite side ofapparatus 110 as sensor 220 (e.g., the rear side of apparatus 110). Invarious embodiments, apparatus 110 may include function button 430, asshown in FIG. 4D.

Various views of apparatus 110 are illustrated in FIGS. 4E through 4K.For example, FIG. 4E shows a view of apparatus 110 with an electricalconnection 441. Electrical connection 441 may be, for example, a USBport, that may be used to transfer data to/from apparatus 110 andprovide electrical power to apparatus 110. In an example embodiment,connection 441 may be used to charge a battery 442 schematically shownin FIG. 4E. FIG. 4F shows F-view of apparatus 110, including sensor 220and one or more microphones 443. In some embodiments, apparatus 110 mayinclude several microphones 443 facing outwards, wherein microphones 443are configured to obtain environmental sounds and sounds of variousspeakers communicating with user 100. FIG. 4G shows R-view of apparatus110. In some embodiments, microphone 444 may be positioned at the rearside of apparatus 110, as shown in FIG. 4G. Microphone 444 may be usedto detect an audio signal from user 100. It should be noted thatapparatus 110 may have microphones placed at any side (e.g., a frontside, a rear side, a left side, a right side, a top side, or a bottomside) of apparatus 110. In various embodiments, some microphones may beat a first side (e.g., microphones 443 may be at the front of apparatus110) and other microphones may be at a second side (e.g., microphone 444may be at the back side of apparatus 110).

FIGS. 4H and 4I show different sides of apparatus 110 (i.e., S-view ofapparatus 110) consisted with disclosed embodiments. For example, FIG.4H shows the location of sensor 220 and an example shape of clip 431.FIG. 4J shows T-view of apparatus 110, including function button 430,and FIG. 4K shows B-view of apparatus 110 with electrical connection441.

The example embodiments discussed above with respect to FIGS. 3A, 3B,4A, and 4B are not limiting. In some embodiments, apparatus 110 may beimplemented in any suitable configuration for performing the disclosedmethods. For example, referring back to FIG. 2 , the disclosedembodiments may implement an apparatus 110 according to anyconfiguration including an image sensor 220 and a processor unit 210 toperform image analysis and for communicating with a feedback unit 230.

FIG. 5A is a block diagram illustrating the components of apparatus 110according to an example embodiment. As shown in FIG. 5A, and assimilarly discussed above, apparatus 110 includes an image sensor 220, amemory 550, a processor 210, a feedback outputting unit 230, a wirelesstransceiver 530, and a mobile power source 520. In other embodiments,apparatus 110 may also include buttons, other sensors such as amicrophone, and inertial measurements devices such as accelerometers,gyroscopes, magnetometers, temperature sensors, color sensors, lightsensors, etc. Apparatus 110 may further include a data port 570 and apower connection 510 with suitable interfaces for connecting with anexternal power source or an external device (not shown).

Processor 210, depicted in FIG. 5A, may include any suitable processingdevice. The term “processing device” includes any physical device havingan electric circuit that performs a logic operation on input or inputs.For example, processing device may include one or more integratedcircuits, microchips, microcontrollers, microprocessors, all or part ofa central processing unit (CPU), graphics processing unit (GPU), digitalsignal processor (DSP), field-programmable gate array (FPGA), or othercircuits suitable for executing instructions or performing logicoperations. The instructions executed by the processing device may, forexample, be pre-loaded into a memory integrated with or embedded intothe processing device or may be stored in a separate memory (e.g.,memory 550). Memory 550 may comprise a Random Access Memory (RAM), aRead-Only Memory (ROM), a hard disk, an optical disk, a magnetic medium,a flash memory, other permanent, fixed, or volatile memory, or any othermechanism capable of storing instructions.

Although, in the embodiment illustrated in FIG. 5A, apparatus 110includes one processing device (e.g., processor 210), apparatus 110 mayinclude more than one processing device. Each processing device may havea similar construction, or the processing devices may be of differingconstructions that are electrically connected or disconnected from eachother. For example, the processing devices may be separate circuits orintegrated in a single circuit. When more than one processing device isused, the processing devices may be configured to operate independentlyor collaboratively. The processing devices may be coupled electrically,magnetically, optically, acoustically, mechanically or by other meansthat permit them to interact.

In some embodiments, processor 210 may process a plurality of imagescaptured from the environment of user 100 to determine differentparameters related to capturing subsequent images. For example,processor 210 can determine, based on information derived from capturedimage data, a value for at least one of the following: an imageresolution, a compression ratio, a cropping parameter, frame rate, afocus point, an exposure time, an aperture size, and a lightsensitivity. The determined value may be used in capturing at least onesubsequent image. Additionally, processor 210 can detect imagesincluding at least one hand-related trigger in the environment of theuser and perform an action and/or provide an output of information to auser via feedback outputting unit 230.

In another embodiment, processor 210 can change the aiming direction ofimage sensor 220. For example, when apparatus 110 is attached with clip420, the aiming direction of image sensor 220 may not coincide with thefield-of-view of user 100. Processor 210 may recognize certainsituations from the analyzed image data and adjust the aiming directionof image sensor 220 to capture relevant image data. For example, in oneembodiment, processor 210 may detect an interaction with anotherindividual and sense that the individual is not fully in view, becauseimage sensor 220 is tilted down. Responsive thereto, processor 210 mayadjust the aiming direction of image sensor 220 to capture image data ofthe individual. Other scenarios are also contemplated where processor210 may recognize the need to adjust an aiming direction of image sensor220.

In some embodiments, processor 210 may communicate data tofeedback-outputting unit 230, which may include any device configured toprovide information to a user 100. Feedback outputting unit 230 may beprovided as part of apparatus 110 (as shown) or may be provided externalto apparatus 110 and communicatively coupled thereto.Feedback-outputting unit 230 may be configured to output visual ornonvisual feedback based on signals received from processor 210, such aswhen processor 210 recognizes a hand-related trigger in the analyzedimage data.

The term “feedback” refers to any output or information provided inresponse to processing at least one image in an environment. In someembodiments, as similarly described above, feedback may include anaudible or visible indication of time information, detected text ornumerals, the value of currency, a branded product, a person's identity,the identity of a landmark or other environmental situation or conditionincluding the street names at an intersection or the color of a trafficlight, etc., as well as other information associated with each of these.For example, in some embodiments, feedback may include additionalinformation regarding the amount of currency still needed to complete atransaction, information regarding the identified person, historicalinformation or times and prices of admission etc. of a detected landmarketc. In some embodiments, feedback may include an audible tone, atactile response, and/or information previously recorded by user 100.Feedback-outputting unit 230 may comprise appropriate components foroutputting acoustical and tactile feedback. For example,feedback-outputting unit 230 may comprise audio headphones, a hearingaid type device, a speaker, a bone conduction headphone, interfaces thatprovide tactile cues, vibrotactile stimulators, etc. In someembodiments, processor 210 may communicate signals with an externalfeedback outputting unit 230 via a wireless transceiver 530, a wiredconnection, or some other communication interface. In some embodiments,feedback outputting unit 230 may also include any suitable displaydevice for visually displaying information to user 100.

As shown in FIG. 5A, apparatus 110 includes memory 550. Memory 550 mayinclude one or more sets of instructions accessible to processor 210 toperform the disclosed methods, including instructions for recognizing ahand-related trigger in the image data. In some embodiments memory 550may store image data (e.g., images, videos) captured from theenvironment of user 100. In addition, memory 550 may store informationspecific to user 100, such as image representations of knownindividuals, favorite products, personal items, and calendar orappointment information, etc. In some embodiments, processor 210 maydetermine, for example, which type of image data to store based onavailable storage space in memory 550. In another embodiment, processor210 may extract information from the image data stored in memory 550.

As further shown in FIG. 5A, apparatus 110 includes mobile power source520. The term “mobile power source” includes any device capable ofproviding electrical power, which can be easily carried by hand (e.g.,mobile power source 520 may weigh less than a pound). The mobility ofthe power source enables user 100 to use apparatus 110 in a variety ofsituations. In some embodiments, mobile power source 520 may include oneor more batteries (e.g., nickel-cadmium batteries, nickel-metal hydridebatteries, and lithium-ion batteries) or any other type of electricalpower supply. In other embodiments, mobile power source 520 may berechargeable and contained within a casing that holds apparatus 110. Inyet other embodiments, mobile power source 520 may include one or moreenergy harvesting devices for converting ambient energy into electricalenergy (e.g., portable solar power units, human vibration units, etc.).

Mobile power source 520 may power one or more wireless transceivers(e.g., wireless transceiver 530 in FIG. 5A). The term “wirelesstransceiver” refers to any device configured to exchange transmissionsover an air interface by use of radio frequency, infrared frequency,magnetic field, or electric field. Wireless transceiver 530 may use anyknown standard to transmit and/or receive data (e.g., Wi-Fi, Bluetooth®,Bluetooth Smart, 802.15.4, or ZigBee). In some embodiments, wirelesstransceiver 530 may transmit data (e.g., raw image data, processed imagedata, extracted information) from apparatus 110 to computing device 120and/or server 250. Wireless transceiver 530 may also receive data fromcomputing device 120 and/or server 250. In other embodiments, wirelesstransceiver 530 may transmit data and instructions to an externalfeedback outputting unit 230.

FIG. 5B is a block diagram illustrating the components of apparatus 110according to another example embodiment. In some embodiments, apparatus110 includes a first image sensor 220 a, a second image sensor 220 b, amemory 550, a first processor 210 a, a second processor 210 b, afeedback outputting unit 230, a wireless transceiver 530, a mobile powersource 520, and a power connector 510. In the arrangement shown in FIG.5B, each of the image sensors may provide images in a different imageresolution, or face a different direction. Alternatively, each imagesensor may be associated with a different camera (e.g., a wide-anglecamera, a narrow angle camera, an IR camera, etc.). In some embodiments,apparatus 110 can select which image sensor to use based on variousfactors. For example, processor 210 a may determine, based on availablestorage space in memory 550, to capture subsequent images in a certainresolution.

Apparatus 110 may operate in a first processing-mode and in a secondprocessing-mode, such that the first processing-mode may consume lesspower than the second processing-mode. For example, in the firstprocessing-mode, apparatus 110 may capture images and process thecaptured images to make real-time decisions based on an identifyinghand-related trigger, for example. In the second processing-mode,apparatus 110 may extract information from stored images in memory 550and delete images from memory 550. In some embodiments, mobile powersource 520 may provide more than fifteen hours of processing in thefirst processing-mode and about three hours of processing in the secondprocessing-mode. Accordingly, different processing-modes may allowmobile power source 520 to produce sufficient power for poweringapparatus 110 for various time periods (e.g., more than two hours, morethan four hours, more than ten hours, etc.).

In some embodiments, apparatus 110 may use first processor 210 a in thefirst processing-mode when powered by mobile power source 520, andsecond processor 210 b in the second processing-mode when powered byexternal power source 580 that is connectable via power connector 510.In other embodiments, apparatus 110 may determine, based on predefinedconditions, which processors or which processing modes to use. Apparatus110 may operate in the second processing-mode even when apparatus 110 isnot powered by external power source 580. For example, apparatus 110 maydetermine that it should operate in the second processing-mode whenapparatus 110 is not powered by external power source 580, if theavailable storage space in memory 550 for storing new image data islower than a predefined threshold.

Although one wireless transceiver is depicted in FIG. 5B, apparatus 110may include more than one wireless transceiver (e.g., two wirelesstransceivers). In an arrangement with more than one wirelesstransceiver, each of the wireless transceivers may use a differentstandard to transmit and/or receive data. In some embodiments, a firstwireless transceiver may communicate with server 250 or computing device120 using a cellular standard (e.g., LTE or GSM), and a second wirelesstransceiver may communicate with server 250 or computing device 120using a short-range standard (e.g., Wi-Fi or Bluetooth®). In someembodiments, apparatus 110 may use the first wireless transceiver whenthe wearable apparatus is powered by a mobile power source included inthe wearable apparatus, and use the second wireless transceiver when thewearable apparatus is powered by an external power source.

FIG. 5C is a block diagram illustrating the components of apparatus 110according to another example embodiment including computing device 120.In this embodiment, apparatus 110 includes an image sensor 220, a memory550 a, a first processor 210, a feedback-outputting unit 230, a wirelesstransceiver 530 a, a mobile power source 520, and a power connector 510.As further shown in FIG. 5C, computing device 120 includes a processor540, a feedback-outputting unit 545, a memory 550 b, a wirelesstransceiver 530 b, and a display 260. One example of computing device120 is a smartphone or tablet having a dedicated application installedtherein. In other embodiments, computing device 120 may include anyconfiguration such as an on-board automobile computing system, a PC, alaptop, and any other system consistent with the disclosed embodiments.In this example, user 100 may view feedback output in response toidentification of a hand-related trigger on display 260. Additionally,user 100 may view other data (e.g., images, video clips, objectinformation, schedule information, extracted information, etc.) ondisplay 260. In addition, user 100 may communicate with server 250 viacomputing device 120.

In some embodiments, processor 210 and processor 540 are configured toextract information from captured image data. The term “extractinginformation” includes any process by which information associated withobjects, individuals, locations, events, etc., is identified in thecaptured image data by any means known to those of ordinary skill in theart. In some embodiments, apparatus 110 may use the extractedinformation to send feedback or other real-time indications to feedbackoutputting unit 230 or to computing device 120. In some embodiments,processor 210 may identify in the image data the individual standing infront of user 100, and send computing device 120 the name of theindividual and the last time user 100 met the individual. In anotherembodiment, processor 210 may identify in the image data, one or morevisible triggers, including a hand-related trigger, and determinewhether the trigger is associated with a person other than the user ofthe wearable apparatus to selectively determine whether to perform anaction associated with the trigger. One such action may be to provide afeedback to user 100 via feedback-outputting unit 230 provided as partof (or in communication with) apparatus 110 or via a feedback unit 545provided as part of computing device 120. For example,feedback-outputting unit 545 may be in communication with display 260 tocause the display 260 to visibly output information. In someembodiments, processor 210 may identify in the image data a hand-relatedtrigger and send computing device 120 an indication of the trigger.Processor 540 may then process the received trigger information andprovide an output via feedback outputting unit 545 or display 260 basedon the hand-related trigger. In other embodiments, processor 540 maydetermine a hand-related trigger and provide suitable feedback similarto the above, based on image data received from apparatus 110. In someembodiments, processor 540 may provide instructions or otherinformation, such as environmental information to apparatus 110 based onan identified hand-related trigger.

In some embodiments, processor 210 may identify other environmentalinformation in the analyzed images, such as an individual standing infront user 100, and send computing device 120 information related to theanalyzed information such as the name of the individual and the lasttime user 100 met the individual. In a different embodiment, processor540 may extract statistical information from captured image data andforward the statistical information to server 250. For example, certaininformation regarding the types of items a user purchases, or thefrequency a user patronizes a particular merchant, etc. may bedetermined by processor 540. Based on this information, server 250 maysend computing device 120 coupons and discounts associated with theuser's preferences.

When apparatus 110 is connected or wirelessly connected to computingdevice 120, apparatus 110 may transmit at least part of the image datastored in memory 550 a for storage in memory 550 b. In some embodiments,after computing device 120 confirms that transferring the part of imagedata was successful, processor 540 may delete the part of the imagedata. The term “delete” means that the image is marked as ‘deleted’ andother image data may be stored instead of it, but does not necessarilymean that the image data was physically removed from the memory.

As will be appreciated by a person skilled in the art having the benefitof this disclosure, numerous variations and/or modifications may be madeto the disclosed embodiments. Not all components are essential for theoperation of apparatus 110. Any component may be located in anyappropriate apparatus and the components may be rearranged into avariety of configurations while providing the functionality of thedisclosed embodiments. For example, in some embodiments, apparatus 110may include a camera, a processor, and a wireless transceiver forsending data to another device. Therefore, the foregoing configurationsare examples and, regardless of the configurations discussed above,apparatus 110 can capture, store, and/or process images.

Further, the foregoing and following description refers to storingand/or processing images or image data. In the embodiments disclosedherein, the stored and/or processed images or image data may comprise arepresentation of one or more images captured by image sensor 220. Asthe term is used herein, a “representation” of an image (or image data)may include an entire image or a portion of an image. A representationof an image (or image data) may have the same resolution or a lowerresolution as the image (or image data), and/or a representation of animage (or image data) may be altered in some respect (e.g., becompressed, have a lower resolution, have one or more colors that arealtered, etc.).

For example, apparatus 110 may capture an image and store arepresentation of the image that is compressed as a JPG file. As anotherexample, apparatus 110 may capture an image in color, but store ablack-and-white representation of the color image. As yet anotherexample, apparatus 110 may capture an image and store a differentrepresentation of the image (e.g., a portion of the image). For example,apparatus 110 may store a portion of an image that includes a face of aperson who appears in the image, but that does not substantially includethe environment surrounding the person. Similarly, apparatus 110 may,for example, store a portion of an image that includes a product thatappears in the image, but does not substantially include the environmentsurrounding the product. As yet another example, apparatus 110 may storea representation of an image at a reduced resolution (i.e., at aresolution that is of a lower value than that of the captured image).Storing representations of images may allow apparatus 110 to savestorage space in memory 550. Furthermore, processing representations ofimages may allow apparatus 110 to improve processing efficiency and/orhelp to preserve battery life.

In addition to the above, in some embodiments, any one of apparatus 110or computing device 120, via processor 210 or 540, may further processthe captured image data to provide additional functionality to recognizeobjects and/or gestures and/or other information in the captured imagedata. In some embodiments, actions may be taken based on the identifiedobjects, gestures, or other information. In some embodiments, processor210 or 540 may identify in the image data, one or more visible triggers,including a hand-related trigger, and determine whether the trigger isassociated with a person other than the user to determine whether toperform an action associated with the trigger.

Some embodiments of the present disclosure may include an apparatussecurable to an article of clothing of a user. Such an apparatus mayinclude two portions, connectable by a connector. A capturing unit maybe designed to be worn on the outside of a user's clothing, and mayinclude an image sensor for capturing images of a user's environment.The capturing unit may be connected to or connectable to a power unit,which may be configured to house a power source and a processing device.The capturing unit may be a small device including a camera or otherdevice for capturing images. The capturing unit may be designed to beinconspicuous and unobtrusive, and may be configured to communicate witha power unit concealed by a user's clothing. The power unit may includebulkier aspects of the system, such as transceiver antennas, at leastone battery, a processing device, etc. In some embodiments,communication between the capturing unit and the power unit may beprovided by a data cable included in the connector, while in otherembodiments, communication may be wirelessly achieved between thecapturing unit and the power unit. Some embodiments may permitalteration of the orientation of an image sensor of the capture unit,for example to better capture images of interest.

FIG. 6 illustrates an exemplary embodiment of a memory containingsoftware modules consistent with the present disclosure. Included inmemory 550 are orientation identification module 601, orientationadjustment module 602, and motion tracking module 603. Modules 601, 602,603 may contain software instructions for execution by at least oneprocessing device, e.g., processor 210, included with a wearableapparatus. Orientation identification module 601, orientation adjustmentmodule 602, and motion tracking module 603 may cooperate to provideorientation adjustment for a capturing unit incorporated into wirelessapparatus 110.

FIG. 7 illustrates an exemplary capturing unit 710 including anorientation adjustment unit 705. Orientation adjustment unit 705 may beconfigured to permit the adjustment of image sensor 220. As illustratedin FIG. 7 , orientation adjustment unit 705 may include an eye-ball typeadjustment mechanism. In alternative embodiments, orientation adjustmentunit 705 may include gimbals, adjustable stalks, pivotable mounts, andany other suitable unit for adjusting an orientation of image sensor220.

Image sensor 220 may be configured to be movable with the head of user100 in such a manner that an aiming direction of image sensor 220substantially coincides with a field of view of user 100. For example,as described above, a camera associated with image sensor 220 may beinstalled within capturing unit 710 at a predetermined angle in aposition facing slightly upwards or downwards, depending on an intendedlocation of capturing unit 710. Accordingly, the set aiming direction ofimage sensor 220 may match the field-of-view of user 100. In someembodiments, processor 210 may change the orientation of image sensor220 using image data provided from image sensor 220. For example,processor 210 may recognize that a user is reading a book and determinethat the aiming direction of image sensor 220 is offset from the text.That is, because the words in the beginning of each line of text are notfully in view, processor 210 may determine that image sensor 220 istilted in the wrong direction. Responsive thereto, processor 210 mayadjust the aiming direction of image sensor 220.

Orientation identification module 601 may be configured to identify anorientation of an image sensor 220 of capturing unit 710. An orientationof an image sensor 220 may be identified, for example, by analysis ofimages captured by image sensor 220 of capturing unit 710, by tilt orattitude sensing devices within capturing unit 710, and by measuring arelative direction of orientation adjustment unit 705 with respect tothe remainder of capturing unit 710.

Orientation adjustment module 602 may be configured to adjust anorientation of image sensor 220 of capturing unit 710. As discussedabove, image sensor 220 may be mounted on an orientation adjustment unit705 configured for movement. Orientation adjustment unit 705 may beconfigured for rotational and/or lateral movement in response tocommands from orientation adjustment module 602. In some embodimentsorientation adjustment unit 705 may be adjust an orientation of imagesensor 220 via motors, electromagnets, permanent magnets, and/or anysuitable combination thereof.

In some embodiments, monitoring module 603 may be provided forcontinuous monitoring. Such continuous monitoring may include tracking amovement of at least a portion of an object included in one or moreimages captured by the image sensor. For example, in one embodiment,apparatus 110 may track an object as long as the object remainssubstantially within the field-of-view of image sensor 220. Inadditional embodiments, monitoring module 603 may engage orientationadjustment module 602 to instruct orientation adjustment unit 705 tocontinually orient image sensor 220 towards an object of interest. Forexample, in one embodiment, monitoring module 603 may cause image sensor220 to adjust an orientation to ensure that a certain designated object,for example, the face of a particular person, remains within thefield-of view of image sensor 220, even as that designated object movesabout. In another embodiment, monitoring module 603 may continuouslymonitor an area of interest included in one or more images captured bythe image sensor. For example, a user may be occupied by a certain task,for example, typing on a laptop, while image sensor 220 remains orientedin a particular direction and continuously monitors a portion of eachimage from a series of images to detect a trigger or other event. Forexample, image sensor 210 may be oriented towards a piece of laboratoryequipment and monitoring module 603 may be configured to monitor astatus light on the laboratory equipment for a change in status, whilethe user's attention is otherwise occupied.

In some embodiments consistent with the present disclosure, capturingunit 710 may include a plurality of image sensors 220. The plurality ofimage sensors 220 may each be configured to capture different imagedata. For example, when a plurality of image sensors 220 are provided,the image sensors 220 may capture images having different resolutions,may capture wider or narrower fields of view, and may have differentlevels of magnification. Image sensors 220 may be provided with varyinglenses to permit these different configurations. In some embodiments, aplurality of image sensors 220 may include image sensors 220 havingdifferent orientations. Thus, each of the plurality of image sensors 220may be pointed in a different direction to capture different images. Thefields of view of image sensors 220 may be overlapping in someembodiments. The plurality of image sensors 220 may each be configuredfor orientation adjustment, for example, by being paired with an imageadjustment unit 705. In some embodiments, monitoring module 603, oranother module associated with memory 550, may be configured toindividually adjust the orientations of the plurality of image sensors220 as well as to turn each of the plurality of image sensors 220 on oroff as may be required. In some embodiments, monitoring an object orperson captured by an image sensor 220 may include tracking movement ofthe object across the fields of view of the plurality of image sensors220.

Embodiments consistent with the present disclosure may includeconnectors configured to connect a capturing unit and a power unit of awearable apparatus. Capturing units consistent with the presentdisclosure may include least one image sensor configured to captureimages of an environment of a user. Power units consistent with thepresent disclosure may be configured to house a power source and/or atleast one processing device. Connectors consistent with the presentdisclosure may be configured to connect the capturing unit and the powerunit, and may be configured to secure the apparatus to an article ofclothing such that the capturing unit is positioned over an outersurface of the article of clothing and the power unit is positionedunder an inner surface of the article of clothing. Exemplary embodimentsof capturing units, connectors, and power units consistent with thedisclosure are discussed in further detail with respect to FIGS. 8-14 .

FIG. 8 is a schematic illustration of an embodiment of wearableapparatus 110 securable to an article of clothing consistent with thepresent disclosure. As illustrated in FIG. 8 , capturing unit 710 andpower unit 720 may be connected by a connector 730 such that capturingunit 710 is positioned on one side of an article of clothing 750 andpower unit 720 is positioned on the opposite side of the clothing 750.In some embodiments, capturing unit 710 may be positioned over an outersurface of the article of clothing 750 and power unit 720 may be locatedunder an inner surface of the article of clothing 750. The power unit720 may be configured to be placed against the skin of a user.

Capturing unit 710 may include an image sensor 220 and an orientationadjustment unit 705 (as illustrated in FIG. 7 ). Power unit 720 mayinclude mobile power source 520 and processor 210. Power unit 720 mayfurther include any combination of elements previously discussed thatmay be a part of wearable apparatus 110, including, but not limited to,wireless transceiver 530, feedback outputting unit 230, memory 550, anddata port 570.

Connector 730 may include a clip 715 or other mechanical connectiondesigned to clip or attach capturing unit 710 and power unit 720 to anarticle of clothing 750 as illustrated in FIG. 8 . As illustrated, clip715 may connect to each of capturing unit 710 and power unit 720 at aperimeter thereof, and may wrap around an edge of the article ofclothing 750 to affix the capturing unit 710 and power unit 720 inplace. Connector 730 may further include a power cable 760 and a datacable 770. Power cable 760 may be capable of conveying power from mobilepower source 520 to image sensor 220 of capturing unit 710. Power cable760 may also be configured to provide power to any other elements ofcapturing unit 710, e.g., orientation adjustment unit 705. Data cable770 may be capable of conveying captured image data from image sensor220 in capturing unit 710 to processor 800 in the power unit 720. Datacable 770 may be further capable of conveying additional data betweencapturing unit 710 and processor 800, e.g., control instructions fororientation adjustment unit 705.

FIG. 9 is a schematic illustration of a user 100 wearing a wearableapparatus 110 consistent with an embodiment of the present disclosure.As illustrated in FIG. 9 , capturing unit 710 is located on an exteriorsurface of the clothing 750 of user 100. Capturing unit 710 is connectedto power unit 720 (not seen in this illustration) via connector 730,which wraps around an edge of clothing 750.

In some embodiments, connector 730 may include a flexible printedcircuit board (PCB). FIG. 10 illustrates an exemplary embodiment whereinconnector 730 includes a flexible printed circuit board 765. Flexibleprinted circuit board 765 may include data connections and powerconnections between capturing unit 710 and power unit 720. Thus, in someembodiments, flexible printed circuit board 765 may serve to replacepower cable 760 and data cable 770. In alternative embodiments, flexibleprinted circuit board 765 may be included in addition to at least one ofpower cable 760 and data cable 770. In various embodiments discussedherein, flexible printed circuit board 765 may be substituted for, orincluded in addition to, power cable 760 and data cable 770.

FIG. 11 is a schematic illustration of another embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure. As illustrated in FIG. 11 , connector 730 may becentrally located with respect to capturing unit 710 and power unit 720.Central location of connector 730 may facilitate affixing apparatus 110to clothing 750 through a hole in clothing 750 such as, for example, abutton-hole in an existing article of clothing 750 or a specialty holein an article of clothing 750 designed to accommodate wearable apparatus110.

FIG. 12 is a schematic illustration of still another embodiment ofwearable apparatus 110 securable to an article of clothing. Asillustrated in FIG. 12 , connector 730 may include a first magnet 731and a second magnet 732. First magnet 731 and second magnet 732 maysecure capturing unit 710 to power unit 720 with the article of clothingpositioned between first magnet 731 and second magnet 732. Inembodiments including first magnet 731 and second magnet 732, powercable 760 and data cable 770 may also be included. In these embodiments,power cable 760 and data cable 770 may be of any length, and may providea flexible power and data connection between capturing unit 710 andpower unit 720. Embodiments including first magnet 731 and second magnet732 may further include a flexible PCB 765 connection in addition to orinstead of power cable 760 and/or data cable 770. In some embodiments,first magnet 731 or second magnet 732 may be replaced by an objectcomprising a metal material.

FIG. 13 is a schematic illustration of yet another embodiment of awearable apparatus 110 securable to an article of clothing. FIG. 13illustrates an embodiment wherein power and data may be wirelesslytransferred between capturing unit 710 and power unit 720. Asillustrated in FIG. 13 , first magnet 731 and second magnet 732 may beprovided as connector 730 to secure capturing unit 710 and power unit720 to an article of clothing 750. Power and/or data may be transferredbetween capturing unit 710 and power unit 720 via any suitable wirelesstechnology, for example, magnetic and/or capacitive coupling, near fieldcommunication technologies, radiofrequency transfer, and any otherwireless technology suitable for transferring data and/or power acrossshort distances.

FIG. 14 illustrates still another embodiment of wearable apparatus 110securable to an article of clothing 750 of a user. As illustrated inFIG. 14 , connector 730 may include features designed for a contact fit.For example, capturing unit 710 may include a ring 733 with a hollowcenter having a diameter slightly larger than a disk-shaped protrusion734 located on power unit 720. When pressed together with fabric of anarticle of clothing 750 between them, disk-shaped protrusion 734 may fittightly inside ring 733, securing capturing unit 710 to power unit 720.FIG. 14 illustrates an embodiment that does not include any cabling orother physical connection between capturing unit 710 and power unit 720.In this embodiment, capturing unit 710 and power unit 720 may transferpower and data wirelessly. In alternative embodiments, capturing unit710 and power unit 720 may transfer power and data via at least one ofcable 760, data cable 770, and flexible printed circuit board 765.

FIG. 15 illustrates another aspect of power unit 720 consistent withembodiments described herein. Power unit 720 may be configured to bepositioned directly against the user's skin. To facilitate suchpositioning, power unit 720 may further include at least one surfacecoated with a biocompatible material 740. Biocompatible materials 740may include materials that will not negatively react with the skin ofthe user when worn against the skin for extended periods of time. Suchmaterials may include, for example, silicone, PTFE, Kapton®, polyimide,titanium, nitinol, platinum, and others. Also as illustrated in FIG. 15, power unit 720 may be sized such that an inner volume of the powerunit is substantially filled by mobile power source 520. That is, insome embodiments, the inner volume of power unit 720 may be such thatthe volume does not accommodate any additional components except formobile power source 520. In some embodiments, mobile power source 520may take advantage of its close proximity to the skin of user's skin.For example, mobile power source 520 may use the Peltier effect toproduce power and/or charge the power source.

In further embodiments, an apparatus securable to an article of clothingmay further include protective circuitry associated with power source520 housed in in power unit 720. FIG. 16 illustrates an exemplaryembodiment including protective circuitry 775. As illustrated in FIG. 16, protective circuitry 775 may be located remotely with respect to powerunit 720. In alternative embodiments, protective circuitry 775 may alsobe located in capturing unit 710, on flexible printed circuit board 765,or in power unit 720.

Protective circuitry 775 may be configured to protect image sensor 220and/or other elements of capturing unit 710 from potentially dangerouscurrents and/or voltages produced by mobile power source 520. Protectivecircuitry 775 may include passive components such as capacitors,resistors, diodes, inductors, etc., to provide protection to elements ofcapturing unit 710. In some embodiments, protective circuitry 775 mayalso include active components, such as transistors, to provideprotection to elements of capturing unit 710. For example, in someembodiments, protective circuitry 775 may comprise one or more resistorsserving as fuses. Each fuse may comprise a wire or strip that melts(thereby breaking a connection between circuitry of image capturing unit710 and circuitry of power unit 720) when current flowing through thefuse exceeds a predetermined limit (e.g., 500 milliamps, 900 milliamps,1 amp, 1.1 amps, 2 amp, 2.1 amps, 3 amps, etc.) Any or all of thepreviously described embodiments may incorporate protective circuitry775.

In some embodiments, the wearable apparatus may transmit data to acomputing device (e.g., a smartphone, tablet, watch, computer, etc.)over one or more networks via any known wireless standard (e.g.,cellular, Wi-Fi, Bluetooth®, etc.), or via near-filed capacitivecoupling, other short range wireless techniques, or via a wiredconnection. Similarly, the wearable apparatus may receive data from thecomputing device over one or more networks via any known wirelessstandard (e.g., cellular, Wi-Fi, Bluetooth®, etc.), or via near-filedcapacitive coupling, other short range wireless techniques, or via awired connection. The data transmitted to the wearable apparatus and/orreceived by the wireless apparatus may include images, portions ofimages, identifiers related to information appearing in analyzed imagesor associated with analyzed audio, or any other data representing imageand/or audio data. For example, an image may be analyzed and anidentifier related to an activity occurring in the image may betransmitted to the computing device (e.g., the “paired device”). In theembodiments described herein, the wearable apparatus may process imagesand/or audio locally (on board the wearable apparatus) and/or remotely(via a computing device). Further, in the embodiments described herein,the wearable apparatus may transmit data related to the analysis ofimages and/or audio to a computing device for further analysis, display,and/or transmission to another device (e.g., a paired device). Further,a paired device may execute one or more applications (apps) to process,display, and/or analyze data (e.g., identifiers, text, images, audio,etc.) received from the wearable apparatus.

Some of the disclosed embodiments may involve systems, devices, methods,and software products for determining at least one keyword. For example,at least one keyword may be determined based on data collected byapparatus 110. At least one search query may be determined based on theat least one keyword. The at least one search query may be transmittedto a search engine.

In some embodiments, at least one keyword may be determined based on atleast one or more images captured by image sensor 220. In some cases,the at least one keyword may be selected from a keywords pool stored inmemory. In some cases, optical character recognition (OCR) may beperformed on at least one image captured by image sensor 220, and the atleast one keyword may be determined based on the OCR result. In somecases, at least one image captured by image sensor 220 may be analyzedto recognize: a person, an object, a location, a scene, and so forth.Further, the at least one keyword may be determined based on therecognized person, object, location, scene, etc. For example, the atleast one keyword may comprise: a person's name, an object's name, aplace's name, a date, a sport team's name, a movie's name, a book'sname, and so forth.

In some embodiments, at least one keyword may be determined based on theuser's behavior. The user's behavior may be determined based on ananalysis of the one or more images captured by image sensor 220. In someembodiments, at least one keyword may be determined based on activitiesof a user and/or other person. The one or more images captured by imagesensor 220 may be analyzed to identify the activities of the user and/orthe other person who appears in one or more images captured by imagesensor 220. In some embodiments, at least one keyword may be determinedbased on at least one or more audio segments captured by apparatus 110.In some embodiments, at least one keyword may be determined based on atleast GPS information associated with the user. In some embodiments, atleast one keyword may be determined based on at least the current timeand/or date.

In some embodiments, at least one search query may be determined basedon at least one keyword. In some cases, the at least one search querymay comprise the at least one keyword. In some cases, the at least onesearch query may comprise the at least one keyword and additionalkeywords provided by the user. In some cases, the at least one searchquery may comprise the at least one keyword and one or more images, suchas images captured by image sensor 220. In some cases, the at least onesearch query may comprise the at least one keyword and one or more audiosegments, such as audio segments captured by apparatus 110.

In some embodiments, the at least one search query may be transmitted toa search engine. In some embodiments, search results provided by thesearch engine in response to the at least one search query may beprovided to the user. In some embodiments, the at least one search querymay be used to access a database.

For example, in one embodiment, the keywords may include a name of atype of food, such as quinoa, or a brand name of a food product; and thesearch will output information related to desirable quantities ofconsumption, facts about the nutritional profile, and so forth. Inanother example, in one embodiment, the keywords may include a name of arestaurant, and the search will output information related to therestaurant, such as a menu, opening hours, reviews, and so forth. Thename of the restaurant may be obtained using OCR on an image of signage,using GPS information, and so forth. In another example, in oneembodiment, the keywords may include a name of a person, and the searchwill provide information from a social network profile of the person.The name of the person may be obtained using OCR on an image of a nametag attached to the person's shirt, using face recognition algorithms,and so forth. In another example, in one embodiment, the keywords mayinclude a name of a book, and the search will output information relatedto the book, such as reviews, sales statistics, information regardingthe author of the book, and so forth. In another example, in oneembodiment, the keywords may include a name of a movie, and the searchwill output information related to the movie, such as reviews, boxoffice statistics, information regarding the cast of the movie, showtimes, and so forth. In another example, in one embodiment, the keywordsmay include a name of a sport team, and the search will outputinformation related to the sport team, such as statistics, latestresults, future schedule, information regarding the players of the sportteam, and so forth. For example, the name of the sports team may beobtained using audio recognition algorithms.

A wearable apparatus consistent with the disclosed embodiments may beused in social events to identify individuals in the environment of auser of the wearable apparatus and provide contextual informationassociated with the individual. For example, the wearable apparatus maydetermine whether an individual is known to the user, or whether theuser has previously interacted with the individual. The wearableapparatus may provide an indication to the user about the identifiedperson, such as a name of the individual or other identifyinginformation. The device may also extract any information relevant to theindividual, for example, words extracted from a previous encounterbetween the user and the individual, topics discussed during theencounter, or the like. The device may also extract and displayinformation from external source, such as the internet. Further,regardless of whether the individual is known to the user or not, thewearable apparatus may pull available information about the individual,such as from a web page, a social network, etc. and provide theinformation to the user.

This content information may be beneficial for the user when interactingwith the individual. For example, the content information may remind theuser who the individual is. For example, the content information mayinclude a name of the individual, or topics discussed with theindividual, which may remind the user of how he or she knows theindividual. Further, the content information may provide talking pointsfor the user when conversing with the individual. for example, the usermay recall previous topics discussed with the individual, which the usermay want to bring up again. In some embodiments, for example where thecontent information is derived from a social media or blog post, theuser may bring up topics that the user and the individual have notdiscussed yet, such as an opinion or point of view of the individual,events in the individual's life, or other similar information. Thus, thedisclosed embodiments may provide, among other advantages, improvedefficiency, convenience, and functionality over prior art devices.

In some embodiments, apparatus 110 may be configured to use audioinformation in addition to image information. For example, apparatus 110may detect and capture sounds in the environment of the user, via one ormore microphones. Apparatus 110 may use this audio information insteadof, or in combination with, image information to determine situations,identify persons, perform activities, or the like. FIG. 17A is a blockdiagram illustrating components of wearable apparatus 110 according toan example embodiment. FIG. 17A may include the features shown in FIG.5A. For example, as discussed in greater detail above, wearableapparatus may include processor 210, image sensor 220, memory 550,wireless transceiver 530 and various other components as shown in FIG.17A. Wearable apparatus may further comprise an audio sensor 1710. Audiosensor 1710 may be any device capable of capturing sounds from anenvironment of a user and converting them to one or more audio signals.For example, audio sensor 1710 may comprise a microphone or anothersensor (e.g., a pressure sensor, which may encode pressure differencescomprising sound) configured to encode sound waves as a digital signal.As shown in FIG. 17A, processor 210 may analyze signals from audiosensor 1710 in addition to signals from image sensor 220.

FIG. 17B is a block diagram illustrating the components of apparatus 110according to another example embodiment. Similar to FIG. 17A, FIG. 17Bincludes all the features of FIG. 5B along with audio sensor 1710.Processor 210 a may analyze signals from audio sensor 1710 in additionto signals from image sensors 210 a and 210 b. In addition, althoughFIGS. 17A and 17B each depict a single audio sensor, a plurality ofaudio sensors may be used, whether with a single image sensor as in FIG.17A or with a plurality of image sensors as in FIG. 17B.

FIG. 17C is a block diagram illustrating components of wearableapparatus 110 according to an example embodiment. FIG. 17C includes allthe features of FIG. 5C along with audio sensor 1710. As shown in FIG.17C, wearable apparatus 110 may communicate with a computing device 120.In such embodiments, wearable apparatus 110 may send data from audiosensor 1710 to computing device 120 for analysis in addition to or inlieu of analyze the signals using processor 210.

Camera-Based Directional Hearing Aid

As discussed previously, the disclosed embodiments may include providingfeedback, such as acoustical and tactile feedback, to one or moreauxiliary devices in response to processing at least one image in anenvironment. In some embodiments, the auxiliary device may be anearpiece or other device used to provide auditory feedback to the user,such as a hearing aid. Traditional hearing aids often use microphones toamplify sounds in the user's environment. These traditional systems,however, are often unable to distinguish between sounds that may be ofparticular importance to the wearer of the device, or may do so on alimited basis. Using the systems and methods of the disclosedembodiments, various improvements to traditional hearing aids areprovided, as described in detail below.

In one embodiment, a camera-based directional hearing aid may beprovided for selectively amplifying sounds based on a look direction ofa user. The hearing aid may communicate with an image capturing device,such as apparatus 110, to determine the look direction of the user. Thislook direction may be used to isolate and/or selectively amplify soundsreceived from that direction (e.g., sounds from individuals in theuser's look direction, etc.). Sounds received from directions other thanthe user's look direction may be suppressed, attenuated, filtered or thelike.

FIG. 18 is a schematic illustration of an example of a user 100 wearingan apparatus 110 for a camera-based hearing interface device 1810according to a disclosed embodiment. User 100 may wear apparatus 110that is physically connected to a shirt or other piece of clothing ofuser 100, as shown. Consistent with the disclosed embodiments, apparatus110 may be positioned in other locations, as described previously. Forexample, apparatus 110 may be physically connected to a necklace, abelt, glasses, a wrist strap, a button, etc. Apparatus 110 may beconfigured to communicate with a hearing interface device such ashearing interface device 1810. Such communication may be through a wiredconnection, or may be made wirelessly (e.g., using a Bluetooth™, NFC, orforms of wireless communication). In some embodiments, one or moreadditional devices may also be included, such as computing device 120(e.g., FIG. 17C). Accordingly, one or more of the processes or functionsdescribed herein with respect to apparatus 110 or processor 210 may beperformed by computing device 120 and/or processor 540.

Hearing interface device 1810 may be any device configured to provideaudible feedback to user 100. Hearing interface device 1810 maycorrespond to feedback outputting unit 230, described above, andtherefore any descriptions of feedback outputting unit 230 may alsoapply to hearing interface device 1810. In some embodiments, hearinginterface device 1810 may be separate from feedback outputting unit 230and may be configured to receive signals from feedback outputting unit230. As shown in FIG. 18 , hearing interface device 1810 may be placedin one or both ears of user 100, like traditional hearing interfacedevices. Hearing interface device 1810 may be of various styles,including in-the-canal, completely-in-canal, in-the-ear, behind-the-ear,on-the-ear, receiver-in-canal, open fit, or various other styles.Hearing interface device 1810 may include one or more speakers forproviding audible feedback to user 100, microphones for detecting soundsin the environment of user 100, internal electronics, processors,memories, etc. In some embodiments, in addition to or instead of amicrophone, hearing interface device 1810 may comprise one or morecommunication units, and in particular one or more receivers forreceiving signals from apparatus 110 and transferring the signals touser 100.

Hearing interface device 1810 may have various other configurations orplacement locations. In some embodiments, hearing interface device 1810may comprise a bone conduction headphone 1811, as shown in FIG. 18 .Bone conduction headphone 1811 may be surgically implanted and mayprovide audible feedback to user 100 through bone conduction of soundvibrations to the inner ear. Hearing interface device 1810 may alsocomprise one or more headphones (e.g., wireless headphones, over-earheadphones, etc.) or a portable speaker carried or worn by user 100. Insome embodiments, hearing interface device 1810 may be integrated intoother devices, such as a Bluetooth™ headset of the user, glasses, ahelmet (e.g., motorcycle helmets, bicycle helmets, etc.), a hat, etc.

Apparatus 110 may be configured to determine a user look direction 1850of user 100. In some embodiments, user look direction 1850 may betracked by monitoring a direction of the chin, or another body part orface part of user 100 relative to an optical axis of image a camerasensor 1851 (or image sensor 220) of apparatus 110. Apparatus 110 may beconfigured to capture one or more images of the surrounding environmentof user, for example, using image sensor 220. The captured images mayinclude a representation of a chin of user 100, which may be used todetermine user look direction 1850. Processor 210 (and/or processors 210a and 210 b) may be configured to analyze the captured images and detectthe chin or another part of user 100 using various image detection orprocessing algorithms (e.g., using convolutional neural networks (CNN),scale-invariant feature transform (SIFT), histogram of orientedgradients (HOG) features, or other techniques). Based on the detectedrepresentation of a chin of user 100, look direction 1850 may bedetermined. Look direction 1850 may be determined in part by comparingthe detected representation of a chin of user 100 to an optical axis ofa camera sensor 1851. For example, the optical axis 1851 may be known orfixed in each image and processor 210 may determine look direction 1850by comparing a representative angle of the chin of user 100 to thedirection of optical axis 1851. While the process is described using arepresentation of a chin of user 100, various other features may bedetected for determining user look direction 1850, including the user'sface, nose, eyes, hand, etc.

In other embodiments, user look direction 1850 may be aligned moreclosely with the optical axis 1851. For example, as discussed above,apparatus 110 may be affixed to a pair of glasses of user 100, as shownin FIG. 1A. In this embodiment, user look direction 1850 may be the sameas or close to the direction of optical axis 1851. Accordingly, userlook direction 1850 may be determined or approximated based on the viewof image sensor 220.

FIG. 19 is a schematic illustration of an embodiment of an apparatussecurable to an article of clothing consistent with the presentdisclosure. Apparatus 110 may be securable to a piece of clothing, suchas the shirt of user 110, as shown in FIG. 18 . Apparatus 110 may besecurable to other articles of clothing, such as a belt or pants of user100, as discussed above. Apparatus 110 may have one or more cameras1930, which may correspond to image sensor 220. Camera 1930 may beconfigured to capture images of the surrounding environment of user 100.In some embodiments, camera 1930 may be configured to detect arepresentation of a chin of the user in the same images capturing thesurrounding environment of the user, which may be used for otherfunctions described in this disclosure. In other embodiments camera 1930may be an auxiliary or separate camera dedicated to determining userlook direction 1850 (see FIG. 18 ).

Apparatus 110 may further comprise one or more microphones 1920 forcapturing sounds from the environment of user 100. Microphone 1920 mayalso be configured to determine a directionality of sounds in theenvironment of user 100. For example, microphone 1920 may comprise oneor more directional microphones, which may be more sensitive to pickingup sounds in certain directions. For example, microphone 1920 maycomprise a unidirectional microphone, designed to pick up sound from asingle direction or small range of directions. Microphone 1920 may alsocomprise a cardioid microphone, which may be sensitive to sounds fromthe front and sides. Microphone 1920 may also include a microphonearray, which may comprise additional microphones, such as microphone1921 on the front of apparatus 110, or microphone 1922, placed on theside of apparatus 110. In some embodiments, microphone 1920 may be amulti-port microphone for capturing multiple audio signals. Themicrophones shown in FIG. 19 are by way of example only, and anysuitable number, configuration, or location of microphones may beutilized. Processor 210 may be configured to distinguish sounds withinthe environment of user 100 and determine an approximate directionalityof each sound. For example, using an array of microphones 1920,processor 210 may compare the relative timing or amplitude of anindividual sound among the microphones 1920 to determine adirectionality relative to apparatus 100.

Based on the determined user look direction 1850 (see FIG. 18 ),processor 210 may selectively condition or amplify sounds from a regionassociated with user look direction 1850. FIG. 20 is a schematicillustration showing an exemplary environment for use of a camera-basedhearing aid (e.g., of apparatus 110) consistent with the presentdisclosure. Microphone 1920 may detect one or more sounds 2020, 2021,and 2022 within the environment of user 100. Based on user lookdirection 1850, determined by processor 210, a region 2030 associatedwith user look direction 1850 may be determined. As shown in FIG. 20 ,region 2030 may be defined by a cone or range of directions (or angles)based on user look direction 1850. The range of angles may be defined byan angle, θ, as shown in FIG. 20 . The angle, θ, may be any suitableangle for defining a range for conditioning sounds within theenvironment of user 100 (e.g., 10 degrees, 20 degrees, 45 degrees, 90degrees, etc.).

Processor 210 may be configured to cause selective conditioning ofsounds in the environment of user 100 based on region 2030. Theconditioned audio signal may be transmitted to hearing interface device1810 (see also FIG. 18 ) of user 100, and thus may provide user 100 withaudible feedback corresponding to the look direction of the user. Forexample, processor 210 may determine that sound 2020 (which, forexample, may correspond to the voice of an individual 2010, sound fromthe vicinity of individual 2010, etc.) is within region 2030. When it isdetermined that the received sound 2020 is from within region 2030,processor 210 may perform various conditioning techniques on the audiosignals (corresponding to the received sound 2020) received frommicrophone 1920. The conditioning may include amplifying the audiosignals determined to correspond to sound 2020 relative to other audiosignals. Amplification may be accomplished digitally, for example byprocessing audio signals associated with sound 2020 relative to othersignals. In some embodiments, the audio signals corresponding to sound2020 may be amplified more that the audio signals corresponding tosounds emanating from outside region 2030 (such as, for example, audiosignals corresponding to sounds 2021 and 2022). Amplification may alsobe accomplished by changing one or more parameters (e.g., gain, etc.) ofmicrophone 1920 to focus on audio sounds emanating from region 2030(e.g., a region of interest) associated with user look direction 1850.For example, microphone 1920 may be a directional microphone andprocessor 210 may perform an operation to focus microphone 1920 on sound2020 or other sounds within region 2030. Various other techniques foramplifying sound 2020 may be used, such as using a beamformingmicrophone array, acoustic telescope techniques, etc.

Conditioning may also include attenuation or suppressing one or moreaudio signals received from directions outside of region 2030. Forexample, processor 210 may attenuate sounds 2021 and 2022. Similar toamplification of sound 2020, attenuation of sounds 2021 and 2022 mayoccur through processing audio signals, or by varying one or moreparameters associated with one or more microphones 1920 to direct focusaway from sounds emanating from outside of region 2030.

In some embodiments, conditioning may further include changing acharacteristic (e.g., tone, etc.) of the audio signals corresponding tosound 2020 to make sound 2020 more perceptible to user 100. For example,user 100 may have lesser sensitivity to tones in a certain range andconditioning of the audio signals may adjust the pitch (or anothersuitable parameter) of sound 2020 to make it more perceptible to user100. For example, user 100 may experience hearing loss in frequenciesabove 10 kHz. Accordingly, processor 210 may remap higher frequencysounds (e.g., sounds at frequency 15 kHz) to a lower frequency (e.g., 10kHz). In some embodiments processor 210 may be configured to change arate of speech associated with one or more audio signals. Accordingly,processor 210 may be configured to detect speech (e.g., human speech)contained within one or more audio signals received by microphone 1920,for example using voice activity detection (VAD) algorithms ortechniques. If sound 2020 is determined to correspond to voice orspeech, for example from individual 2010, processor 220 may beconfigured to vary the playback rate of sound 2020. For example, therate of speech of individual 2010 may be decreased to make the detectedspeech more perceptible to user 100. Various other processing may beperformed, such as modifying the tone of sound 2020 to maintain the samepitch as the original audio signal, or to reduce noise within the audiosignal. If speech recognition has been performed on the audio signalassociated with sound 2020, conditioning may further include modifyingthe audio signal based on the detected speech. For example, processor210 may introduce pauses or increase the duration of pauses betweenwords and/or sentences, which may make the speech easier to understand.It should be noted that, although FIG. 20 illustrates sound 2020 (withspeech) as emanating from a human being (individual 2010), this is not arequirement. In general, processor 210 may be configured to detectspeech from any source (speaker or another sound emanating apparatus,etc.).

The conditioned audio signal may then be transmitted to the user, forexample, to hearing interface device 1810 and reproduced for user 100.In the conditioned audio signal, sound 2020 may be easier to hear foruser 100, louder and/or more easily distinguishable than sounds 2021 and2022, which may represent background noise within the environment. Itshould be noted that, although the conditioned device is described asbeing directed to a hearing aid (e.g., hearing interface device 1810) ofuser 100, this is only exemplary. In general, the conditioned sound fromregion 2030 may be directed to user 100 in any manner. In someembodiments, the conditioned sound may be directed to a headphone(earphone, etc.) or another audio device worn by user. It is alsocontemplated that, in some embodiments, the received sound from a regioncorresponding the user's gaze (e.g., region 2030) may be transcribedinto text (before or after conditioning) and presented to user as text(for example, in a display device of computing device 120). Thus, ingeneral, processor 210 may detect sounds emanating from a region (zone,angular range, etc.) corresponding to the direction of the user's gaze(e.g., user look direction 1850), preferentially condition the sound(e.g., by changing one or more of its characteristics such that thesound is more perceptible to the user), and direct the conditioned soundto the user (for example, via user's hearing interface device 1810,etc.).

FIG. 21 is a flowchart showing an exemplary process 2100 for selectivelyamplifying sounds emanating from a detected look direction of a userconsistent with disclosed embodiments. Process 2100 may be performed byone or more processors associated with apparatus 110, such as processor210. In some embodiments, some or all of process 1900 may be performedon processors external to apparatus 110. In other words, the processorperforming process 2100 may be included in a common housing asmicrophone 1920 and camera 1930, or may be included in a second housing.For example, one or more portions of process 2100 may be performed byprocessors in hearing interface device 1810, or an auxiliary device,such as processor 540 of computing device 120 (see, for example, FIG.17C).

In step 2110, process 2100 may include receiving a plurality of imagesfrom an environment of a user captured by a camera. The camera may be awearable camera such as camera 1930 of apparatus 110. In step 2112,process 2100 may include receiving audio signals representative ofsounds received by at least one microphone. The microphone may beconfigured to capture sounds from an environment of the user. Forexample, the microphone may be microphone 1920, as described above.Accordingly, the microphone may include a directional microphone, amicrophone array, a multi-port microphone, or various other types ofmicrophones. In some embodiments, the microphone and wearable camera maybe included in a common housing, such as the housing of apparatus 110.The one or more processors performing process 2100 may also be includeda second housing (e.g., separate from the common housing of themicrophone and camera). In such embodiments, the processor(s) may beconfigured to receive images and/or audio signals from the commonhousing via a wireless link (e.g., Bluetooth™, NFC, etc.). Accordingly,the common housing (e.g., apparatus 110) and the second housing (e.g.,computing device 120) may further comprise transmitters (e.g.,transceivers 530 a, 530 b of FIG. 17C) or various other communicationcomponents.

In step 2114, process 2100 may include determining a look direction forthe user (or the direction of the user's gaze) based on analysis of atleast one of the plurality of images. As discussed above, varioustechniques may be used to determine the user look direction. In someembodiments, the look direction may be determined based, at least inpart, upon detection of a representation of a chin (or another feature)of a user in one or more images. The images may be processed todetermine a pointing direction of the chin relative to an optical axisof the wearable camera, as discussed above.

In step 2116, process 2100 may include causing selective or preferentialconditioning of at least one audio signal received by the at least onemicrophone from a region associated with the look direction of the user.As described above, the region may be determined based on the user lookdirection determined in step 2114. The range may be associated with anangular width (e.g., arc) about the look direction (e.g., 10 degrees, 20degrees, 45 degrees, 90 degrees, etc.). Various forms of conditioningmay be performed on the audio signal, as discussed above. In someembodiments, conditioning may include changing the tone or playbackspeed of an audio signal. For example, conditioning may include changinga rate of speech associated with the audio signal. In some embodiments,the conditioning may include amplification of the audio signal relativeto other audio signals received from outside of the region associatedwith the look direction of the user. Amplification may be performed byvarious means, such as operation of a directional microphone configuredto focus on audio sounds emanating from the region, or varying one ormore parameters associated with the microphone to cause the microphoneto focus on audio sounds emanating from the region. The amplificationmay include attenuating or suppressing one or more audio signalsreceived by the microphone from directions outside the region associatedwith the look direction of user 110.

In step 2118, process 2100 may include causing transmission of the atleast one conditioned audio signal to a hearing interface deviceconfigured to provide sound to an ear of the user. The conditioned audiosignal, for example, may be transmitted to hearing interface device1810, which may provide sound corresponding to the audio signal to user100. The processor performing process 2100 may further be configured tocause transmission to the hearing interface device of one or more audiosignals representative of background noise, which may be attenuatedrelative to the at least one conditioned audio signal. For example,processor 220 may be configured to transmit audio signals correspondingto sounds 2020, 2021, and 2022. The signal associated with 2020,however, may be modified in a different manner, for example amplified,from sounds 2021 and 2022 based on a determination that sound 2020 isemanating from within region 2030. In some embodiments, hearinginterface device 1810 may include a speaker associated with an earpiece.For example, hearing interface device 1810 may be inserted at leastpartially into the ear of the user for providing audio to the user.Hearing interface device 1810 may also be external to the ear, such as abehind-the-ear hearing device, one or more headphones, a small portablespeaker, or the like. In some embodiments, as illustrated on FIG. 18 ,hearing interface device 1810 may include a bone conduction microphone1811, configured to provide an audio signal to user through vibrationsof a bone of the user's head. Such devices may be placed in contact withthe exterior of the user's skin, or may be implanted surgically andattached to the bone of the user.

Alternatively, or additionally, in some embodiments, in step 2118,process 2100 may include causing the conditioned audio signal to thedisplayed as text on a display device. For example, in some embodiments,computing device 120 may a personal portable electronic device of user(e.g., smart phone, smart watch, laptop, etc.), and in step 2118, theaudio signal may be transcribed and displayed on the display device ofthe portable electronic device for the user to read.

Hearing Aid with Voice and/or Image Recognition

Consistent with the disclosed embodiments, a hearing aid may selectivelyamplify audio signals associated with a voice of a recognizedindividual. The hearing aid system may store voice characteristicsand/or facial features of a recognized person to aid in recognition andselective amplification. For example, when an individual enters thefield of view of apparatus 110, the individual may be recognized as anindividual that has been introduced to the device, or that has possiblyinteracted with user 100 in the past (e.g., a friend, colleague,relative, prior acquaintance, etc.). Accordingly, audio signalsassociated with the recognized individual's voice may be isolated and/orselectively amplified relative to other sounds in the environment of theuser. Audio signals associated with sounds received from directionsother than the recognized individual's direction may be suppressed,attenuated, filtered or the like.

User 100 may wear a hearing aid device (associated with apparatus 110)similar to the camera-based hearing aid device discussed above. Forexample, the hearing aid device may be hearing interface device 1810, asshown in FIG. 18 . Hearing interface device 1810 may be any deviceconfigured to provide audible feedback to user 100. Hearing interfacedevice 1810 may be placed in one or both ears of user 100, similar totraditional hearing interface devices. As discussed above, hearinginterface device 1810 may be of various styles, including in-the-canal,completely-in-canal, in-the-ear, behind-the-ear, on-the-ear,receiver-in-canal, open fit, or various other styles. Hearing interfacedevice 1810 may include one or more speakers for providing audiblefeedback to user 100, a communication unit for receiving signals fromanother system, such as apparatus 110, microphones for detecting soundsin the environment of user 100, internal electronics, processors,memories, etc. Hearing interface device 1810 may correspond to feedbackoutputting unit 230 (see FIG. 17C) or may be separate from feedbackoutputting unit 230 and may be configured to receive signals fromfeedback outputting unit 230.

In some embodiments, hearing interface device 1810 may comprise a boneconduction headphone 1811, as shown in FIG. 18 . Bone conductionheadphone 1811 may be surgically implanted and may provide audiblefeedback to user 100 through bone conduction of sound vibrations to theinner ear. Hearing interface device 1810 may also comprise one or moreheadphones (e.g., wireless headphones, over-ear headphones, etc.) or aportable speaker carried or worn by user 100. In some embodiments,hearing interface device 1810 may be integrated into other devices, suchas a Bluetooth™ headset of the user, glasses, a helmet (e.g., motorcyclehelmets, bicycle helmets, etc.), a hat, etc.

Hearing interface device 1810 may be configured to communicate with acamera device, such as apparatus 110. Such communication may be througha wired connection, or may be made wirelessly (e.g., using a Bluetooth™,NFC, or forms of wireless communication). As discussed above, apparatus110 may be worn by user 100 in various configurations, including beingphysically connected to a shirt, necklace, a belt, glasses, a wriststrap, a button, or other articles associated with user 100. In someembodiments, one or more additional devices may also be included, suchas computing device 120. Accordingly, one or more of the processes orfunctions described herein with respect to apparatus 110 or processor210 may be performed by computing device 120 and/or processor 540.

As discussed above, apparatus 110 may comprise at least one microphoneand at least one image capture device. Apparatus 110 may comprisemicrophone 1920, as described with respect to FIG. 19 . Microphone 1920may be configured to determine a directionality of sounds in theenvironment of user 100. For example, microphone 1920 may comprise oneor more directional microphones, a microphone array, a multi-portmicrophone, or the like. The microphones shown in FIG. 19 are by way ofexample only, and any suitable number, configuration, or location ofmicrophones may be utilized. Processor 210 may be configured todistinguish sounds within the environment of user 100 and determine anapproximate directionality of each sound. For example, using an array ofmicrophones 1920, processor 210 may compare the relative timing oramplitude of an individual sound from all the sounds received from thearray of microphones 1920 to determine a directionality of the soundrelative to apparatus 100. Apparatus 110 may comprise one or morecameras, such as camera 1930 (see FIG. 19 ), which may correspond toimage sensor 220. Camera 1930 may be configured to capture images of thesurrounding environment of user 100.

Apparatus 110 may be configured to recognize an individual in theenvironment of user 100. FIG. 22 is a schematic illustration showing anexemplary environment for use of a hearing aid with voice and/or imagerecognition consistent with the present disclosure. Apparatus 110 may beconfigured to recognize a face 2211 or voice 2212 associated with anindividual 2010 within the environment of user 100. For example,apparatus 110 may be configured to capture one or more images of thesurrounding environment of user 100 using camera 1930 (see FIG. 19 ).The captured images may include a representation of a recognizedindividual 2210, which may be a friend, colleague, relative, oracquaintance of user 100. Processor 210 (and/or processors 210 a and 210b) may be configured to analyze the captured images and detect therecognized user using various facial recognition techniques, asrepresented by element 2211. Accordingly, apparatus 110, or specificallymemory 550 (see FIG. 17A), may comprise one or more facial or voicerecognition components. With reference to FIG. 17C, in some embodiments,apparatus 110 (e.g., memory 550 a) and/or computing device 120 (e.g.,memory 550 b) may include a database containing images and/or voices (orcharacteristics of the images/voices) of different people associatedwith user. And processor 210 may compare a received image from camera1930 and/or microphone 1920 with the images and/or voices stored in thedatabase to recognize the individual.

FIG. 23 illustrates an exemplary embodiment of apparatus 110 comprisingfacial and voice recognition components consistent with the presentdisclosure. Apparatus 110 is shown in FIG. 23 in a simplified form, andapparatus 110 may contain additional elements or may have alternativeconfigurations, for example, as shown in FIGS. 5A-5C, 17A-17C, etc.Memory 550 (or 550 a or 550 b) may include facial recognition component2340 and voice recognition component 2341. These components may beinstead of or in addition to orientation identification module 601,orientation adjustment module 602, and motion tracking module 603 asshown in FIG. 6 . Components 2340 and 2341 may contain softwareinstructions for execution by at least one processing device, e.g.,processor 210, included with a wearable apparatus 110. Components 2340and 2341 are shown within memory 550 by way of example only, and may belocated in other locations within the system. For example, components2340 and 2341 may be located in hearing interface device 1810 (see FIG.18 ), in computing device 120, on a remote server 250 (see FIG. 2 ), orin another device associated with apparatus 110.

Facial recognition component 2340 may be configured to identify one ormore faces within the environment of user 100. For example, withadditional reference to FIG. 22 , facial recognition component 2340 mayidentify facial features on the face 2211 of individual 2010, such asthe eyes, nose, cheekbones, jaw, or other features. Facial recognitioncomponent 2340 may then analyze the relative size and position of thesefeatures to identify the user. Facial recognition component 2340 mayutilize one or more algorithms for analyzing the detected features, suchas principal component analysis (e.g., using eigenfaces), lineardiscriminant analysis, elastic bunch graph matching (e.g., usingFisherface), Local Binary Patterns Histograms (LBPH), Scale InvariantFeature Transform (SIFT), Speed Up Robust Features (SURF), or the like.Other facial recognition techniques such as 3-Dimensional recognition,skin texture analysis, and/or thermal imaging may also be used toidentify individuals. Other features besides facial features may also beused for identification, such as the height, body shape, or otherdistinguishing features of individual 2010.

Facial recognition component 2340 may access a database or dataassociated with user 100 to determine if the detected facial featurescorrespond to a recognized individual. For example, a processor 210 mayaccess a database 2350 (in apparatus 110, computing device 120, computerserver 250, etc.) containing information about individuals known to user100 and data representing associated facial features or otheridentifying features. Such data may include one or more images of theindividuals, or data representative of a face of the user that may beused for identification through facial recognition. Database 2350 may beany device capable of storing information about one or more individuals,and may include a hard drive, a solid state drive, a web storageplatform, a remote server, or the like. Database 2350 may be locatedwithin apparatus 110 (e.g., within memory 550) or external to apparatus110, as shown in FIG. 23 . In some embodiments, database 2050 may beassociated with a social network platform, such as Facebook™, LinkedIn™,Instagram™, etc. Facial recognition component 2340 may also access acontact list of user 100, such as a contact list on the user's phone, aweb-based contact list (e.g., through Outlook™, Skype™, Google™,SalesForce™, etc.) or a dedicated contact list associated with hearinginterface device 1810. In some embodiments, database 2350 may becompiled by apparatus 110 through previous facial recognition analysis.For example, processor 210 may be configured to store data associatedwith one or more faces recognized in images captured by apparatus 110 indatabase 2050. Each time a face is detected in the images, the detectedfacial features or other data may be compared to previously identifiedfaces in database 2350. Facial recognition component 2340 may determinethat an individual is a recognized individual of user 100 if theindividual has previously been recognized by the system in a number ofinstances exceeding a certain threshold, if the individual has beenexplicitly introduced to apparatus 110, or the like.

In some embodiments, user 100 may have access to database 2350, such asthrough a web interface, an application on a mobile device, or throughapparatus 110 or an associated device. For example, user 100 may be ableto select which contacts are recognizable by apparatus 110 and/or deleteor add certain contacts manually. In some embodiments, a user oradministrator may be able to train facial recognition component 2340.For example, user 100 may have an option to confirm or rejectidentifications made by facial recognition component 2340, which mayimprove the accuracy of the system. This training may occur in realtime, as individual 2010 is being recognized, or at some later time.

Other data or information may also inform the facial identificationprocess. In some embodiments, processor 210 may use various techniquesto recognize the voice of individual 2010, as described in furtherdetail below. The recognized voice pattern and the detected facialfeatures may be used, either alone or in combination, to determine thatindividual 2010 is recognized by apparatus 110. Processor 210 may alsodetermine a user look direction 1850, as described above, which may beused to verify the identity of individual 2010. For example, if user 100is looking in the direction of individual 2010 (especially for aprolonged period), this may indicate that individual 2010 is recognizedby user 100, which may be used to increase the confidence of facialrecognition component 2340 or other identification means.

Processor 210 may further be configured to determine whether individual2010 is recognized by user 100 based on one or more detected audiocharacteristics of sounds associated with a voice of individual 2010.Returning to FIG. 22 , processor 210 may determine that sound 2020corresponds to voice 2212 of user 2010. Processor 210 may analyze audiosignals representative of sound 2020 captured by microphone 1920 todetermine whether individual 2010 is recognized by user 100. This may beperformed using voice recognition component 2341 (FIG. 23 ) and mayinclude one or more voice recognition algorithms, such as Hidden MarkovModels, Dynamic Time Warping, neural networks, or other techniques.Voice recognition component and/or processor 210 may access database2350, which may further include a voice print of one or moreindividuals. Voice recognition component 2341 may analyze the audiosignal representative of sound 2020 to determine whether voice 2212matches a voice print of an individual in database 2350. Accordingly,database 2350 may contain voice print data associated with a number ofindividuals, similar to the stored facial identification data describedabove. After determining a match, individual 2010 may be determined tobe a recognized individual of user 100. This process may be used alone,or in conjunction with the facial recognition techniques describedabove. For example, individual 2010 may be recognized using facialrecognition component 2340 and may be verified using voice recognitioncomponent 2341, or vice versa.

In some embodiments, apparatus 110 may detect the voice of an individualthat is not within the field of view of apparatus 110. For example, thevoice may be heard over a speakerphone, from a back seat, or the like.In such embodiments, recognition of an individual may be based on thevoice of the individual only, in the absence of a speaker in the fieldof view. Processor 110 may analyze the voice of the individual asdescribed above, for example, by determining whether the detected voicematches a voice print of an individual in database 2350.

After determining that individual 2010 is a recognized individual ofuser 100, processor 210 may cause selective conditioning of audioassociated with the recognized individual. The conditioned audio signalmay be transmitted to hearing interface device 1810, and thus mayprovide user 100 with audio conditioned based on the recognizedindividual. For example, the conditioning may include amplifying audiosignals determined to correspond to sound 2020 (which may correspond tovoice 2212 of individual 2010) relative to other audio signals. In someembodiments, amplification may be accomplished digitally, for example byprocessing audio signals associated with sound 2020 relative to othersignals. Additionally, or alternatively, amplification may beaccomplished by changing one or more parameters of microphone 1920 tofocus on audio sounds associated with individual 2010. For example,microphone 1920 may be a directional microphone and processor 210 mayperform an operation to focus microphone 1920 on sound 2020. Variousother techniques for amplifying sound 2020 may be used, such as using abeamforming microphone array, acoustic telescope techniques, etc.

In some embodiments, selective conditioning may include attenuation orsuppressing one or more audio signals received from directions notassociated with individual 2010. For example, processor 210 mayattenuate sounds 2021 and/or 2022 (see FIG. 22 ). Similar toamplification of sound 2020, attenuation of sounds may occur throughprocessing audio signals, or by varying one or more parametersassociated with microphone 1920 to direct focus away from sounds notassociated with individual 2010.

Selective conditioning may further include determining whetherindividual 2010 is speaking. For example, processor 210 may beconfigured to analyze images or videos containing representations ofindividual 2010 to determine when individual 2010 is speaking, forexample, based on detected movement of the recognized individual's lips.This may also be determined through analysis of audio signals receivedby microphone 1920, for example by detecting the voice 2212 ofindividual 2010. In some embodiments, the selective conditioning mayoccur dynamically (initiated and/or terminated) based on whether or notthe recognized individual is speaking.

In some embodiments, conditioning may further include changing a tone ofone or more audio signals corresponding to sound 2020 to make the soundmore perceptible to user 100. For example, user 100 may have lessersensitivity to tones in a certain range and conditioning of the audiosignals may adjust the pitch of sound 2020. In some embodimentsprocessor 210 may be configured to change a rate of speech associatedwith one or more audio signals. For example, sound 2020 may bedetermined to correspond to voice 2212 of individual 2010. Processor 210may be configured to vary the rate of speech of individual 2010 to makethe detected speech more perceptible to user 100. Various otherprocessing may be performed, such as modifying the tone of sound 2020 tomaintain the same pitch as the original audio signal, or to reduce noisewithin the audio signal.

In some embodiments, processor 210 may determine a region 2030associated with individual 2010. Region 2030 may be associated with adirection of individual 2010 relative to apparatus 110 or user 100. Thedirection of individual 2010 may be determined using camera 1930 and/ormicrophone 1920 using the methods described above. As shown in FIG. 22 ,region 2030 may be defined by a cone or range of directions based on adetermined direction of individual 2010 and/or user 100. The range ofangles may be defined by an angle, θ, as shown in FIG. 22 . The angle,θ, may be any suitable angle for defining a range for conditioningsounds within the environment of user 100 (e.g., 10 degrees, 20 degrees,45 degrees, 90 degrees, etc.). Region 2030 may be dynamically calculatedas the position of individual 2010 changes relative to apparatus 110 oruser 100. For example, as user 100 turns, or if individual 2010 moveswithin the environment, processor 210 may be configured to trackindividual 2010 within the environment and dynamically update region2030. Region 2030 may be used for selective conditioning, for example byamplifying sounds associated with region 2030 and/or attenuating soundsdetermined to be emanating from outside of region 2030.

The conditioned audio signal may then be transmitted to hearinginterface device 1810 and produced for user 100. Thus, in theconditioned audio signal, sound 2020 (and specifically voice 2212) maybe louder and/or more easily distinguishable than sounds 2021 and 2022,which may represent background noise within the environment.

In some embodiments, processor 210 may perform further analysis based oncaptured images or videos to determine how to selectively conditionaudio signals associated with a recognized individual. In someembodiments, processor 210 may analyze the captured images toselectively condition audio associated with one individual relative toothers. For example, processor 210 may determine the direction of arecognized individual relative to the user based on the images and maydetermine how to selectively condition audio signals associated with theindividual based on the direction. If the recognized individual isstanding to the front of the user, audio associated with that user maybe amplified (or otherwise selectively conditioned) relative to audioassociated with an individual standing to the side of the user.Similarly, processor 210 may selectively condition audio signalsassociated with an individual based on proximity to the user. Processor210 may determine a distance from the user to each individual based oncaptured images and may selectively condition audio signals associatedwith the individuals based on the distance. For example, an individualcloser to the user may be prioritized higher than an individual that isfarther away.

In some embodiments, selective conditioning of audio signals associatedwith a recognized individual may be based on the identities ofindividuals within the environment of the user. For example, wheremultiple individuals are detected in the images, processor 210 may useone or more facial recognition techniques to identify the individuals,as described above. Audio signals associated with individuals that areknown to user 100 may be selectively amplified or otherwise conditionedto have priority over unknown individuals. For example, processor 210may be configured to attenuate or silence audio signals associated withbystanders in the user's environment, such as a noisy office mate, etc.In some embodiments, processor 210 may also determine a hierarchy ofindividuals and give priority based on the relative status of theindividuals. This hierarchy may be based on the individual's positionwithin a family or an organization (e.g., a company, sports team, club,etc.) relative to the user. For example, the user's boss may be rankedhigher than a co-worker or a member of the maintenance staff and thusmay have priority in the selective conditioning process. In someembodiments, the hierarchy may be determined based on a list ordatabase. Individuals recognized by the system may be rankedindividually or grouped into tiers of priority. This database may bemaintained specifically for this purpose, or may be accessed externally.For example, the database may be associated with a social network of theuser (e.g., Facebook™, LinkedIn™, etc.) and individuals may beprioritized based on their grouping or relationship with the user.Individuals identified as “close friends” or family, for example, may beprioritized over acquaintances of the user.

Selective conditioning may be based on a determined behavior of one ormore individuals determined based on the captured images. In someembodiments, processor 210 may be configured to determine a lookdirection of the individuals in the images. Accordingly, the selectiveconditioning may be based on behavior of the other individuals towardsthe recognized individual. For example, processor 210 may selectivelycondition audio associated with a first individual that one or moreother users are looking at. If the attention of the individuals shiftsto a second individual, processor 210 may then switch to selectivelycondition audio associated with the second user. In some embodiments,processor 210 may be configured to selectively condition audio based onwhether a recognized individual is speaking to the user or to anotherindividual. For example, when the recognized individual is speaking tothe user, the selective conditioning may include amplifying an audiosignal associated with the recognized individual relative to other audiosignals received from directions outside a region associated with therecognized individual. When the recognized individual is speaking toanother individual, the selective conditioning may include attenuatingthe audio signal relative to other audio signals received fromdirections outside the region associated with the recognized individual.

In some embodiments, processor 210 may have access to one or more voiceprints of individuals, which may facilitate selective conditioning ofvoice 2212 of individual 2010 in relation to other sounds or voices.Having a speaker's voice print, and a high-quality voice print, mayprovide for fast and efficient speaker separation. A high-quality voiceprint may be collected, for example, when the user speaks alone,preferably in a quiet environment. By having a voice print of one ormore speakers, it is possible to separate an ongoing voice signal almostin real time, e.g., with a minimal delay, using a sliding time window.The delay may be, for example 10 millisecond (ms), 20 ms, 30 ms, 50 ms,100 ms, or the like. Different time windows may be selected, dependingon the quality of the voice print, on the quality of the captured audio,the difference in characteristics between the speaker and otherspeaker(s), the available processing resources, the required separationquality, or the like. In some embodiments, a voice print may beextracted from a segment of a conversation in which an individual speaksalone, and then used for separating the individual's voice later in theconversation, whether the individual's is recognized or not.

Separating voices may be performed as follows: spectral features, alsoreferred to as spectral attributes, spectral envelope, or spectrogrammay be extracted from a clean audio of a single speaker and fed into apre-trained first neural network, which generates or updates a signatureof the speaker's voice based on the extracted features. The audio may befor example, of one second of clean voice. The output signature may be avector representing the speaker's voice, such that the distance betweenthe vector and another vector extracted from the voice of the samespeaker is typically smaller than the distance between the vector and avector extracted from the voice of another speaker. The speaker's modelmay be pre-generated from a captured audio. Alternatively, oradditionally, the model may be generated after a segment of the audio inwhich only the speaker speaks, followed by another segment in which thespeaker and another speaker (or background noise) is heard, and which itis required to separate.

Then, to separate the speaker's voice from additional speakers orbackground noise in a noisy audio, a second pre-trained neural networkmay receive the noisy audio and the speaker's signature, and output anaudio (which may also be represented as attributes) of the voice of thespeaker as extracted from the noisy audio, separated from the otherspeech or background noise. It will be appreciated that the same oradditional neural networks may be used to separate the voices ofmultiple speakers. For example, if there are two possible speakers, twoneural networks may be activated, each with models of the same noisyoutput and one of the two speakers. Alternatively, a neural network mayreceive voice signatures of two or more speakers, and output the voiceof each of the speakers separately. Accordingly, the system may generatetwo or more different audio outputs, each comprising the speech of therespective speaker. In some embodiments, if separation is impossible,the input voice may only be cleaned from background noise.

FIG. 24 is a flowchart showing an exemplary process 2400 for selectivelyamplifying audio signals associated with a voice of a recognizedindividual consistent with disclosed embodiments. Process 2400 may beperformed by one or more processors associated with apparatus 110, suchas processor 210. In some embodiments, some, or all of process 2400 maybe performed on processors external to apparatus 110. In other words,the processor performing process 2400 may be included in the same commonhousing as microphone 1920 and camera 1930, or may be included in asecond housing. For example, one or more portions of process 2400 may beperformed by processors in hearing interface device 1810, or in anauxiliary device, such as computing device 120.

In step 2410, process 2400 may include receiving a plurality of imagesfrom an environment of a user captured by a camera. The images may becaptured by a wearable camera such as camera 1930 of apparatus 110. Instep 2412, process 2400 may include identifying a representation of arecognized individual in at least one of the plurality of images.Individual 2010 may be recognized by processor 210 using facialrecognition component 2340, as described above. For example, individual2010 may be a friend, colleague, relative, or prior acquaintance of theuser. Processor 210 may determine whether an individual represented inat least one of the plurality of images is a recognized individual basedon one or more detected facial features associated with the individual.Processor 210 may also determine whether the individual is recognizedbased on one or more detected audio characteristics of sounds determinedto be associated with a voice of the individual, as described above.

In step 2414, process 2400 may include receiving audio signalsrepresentative of sounds captured by a microphone. For example,apparatus 110 may receive audio signals representative of sounds 2020,2021, and 2022, captured by microphone 1920. Accordingly, the microphonemay include a directional microphone, a microphone array, a multi-portmicrophone, or various other types of microphones, as described above.In some embodiments, the microphone and wearable camera may be includedin a common housing, such as the housing of apparatus 110. The one ormore processors performing process 2400 may also be included in thehousing (e.g., processor 210), or may be included in a second housing.Where a second housing is used, the processor(s) may be configured toreceive images and/or audio signals from the common housing via awireless link (e.g., Bluetooth™, NFC, etc.). Accordingly, the commonhousing (e.g., apparatus 110) and the second housing (e.g., computingdevice 120) may further comprise transmitters, receivers, and/or variousother communication components.

In step 2416, process 2400 may include cause selective conditioning ofat least one audio signal received by the at least one microphone from aregion associated with the at least one recognized individual. Asdescribed above, the region may be determined based on a determineddirection of the recognized individual based one or more of theplurality of images or audio signals. The range may be associated withan angular width about the direction of the recognized individual (e.g.,10 degrees, 20 degrees, 45 degrees, 90 degrees, etc.).

Various forms of conditioning may be performed on the audio signal, asdiscussed above. In some embodiments, conditioning may include changingthe tone or playback speed of an audio signal. For example, conditioningmay include changing a rate of speech associated with the audio signal.In some embodiments, the conditioning may include amplification of theaudio signal relative to other audio signals received from outside ofthe region associated with the recognized individual. Amplification maybe performed by various means, such as operation of a directionalmicrophone configured to focus on audio sounds emanating from the regionor varying one or more parameters associated with the microphone tocause the microphone to focus on audio sounds emanating from the region.The amplification may include attenuating or suppressing one or moreaudio signals received by the microphone from directions outside theregion. In some embodiments, step 2416 may further comprise determining,based on analysis of the plurality of images, that the recognizedindividual is speaking and trigger the selective conditioning based onthe determination that the recognized individual is speaking. Forexample, the determination that the recognized individual is speakingmay be based on detected movement of the recognized individual's lips.In some embodiments, selective conditioning may be based on furtheranalysis of the captured images as described above, for example, basedon the direction or proximity of the recognized individual, the identityof the recognized individual, the behavior of other individuals, etc.

In step 2418, process 2400 may include causing transmission of the atleast one conditioned audio signal to a hearing interface deviceconfigured to provide sound to an ear of the user. The conditioned audiosignal, for example, may be transmitted to hearing interface device1810, which may provide sound corresponding to the audio signal to user100. The processor performing process 2400 may further be configured tocause transmission to the hearing interface device 1810 of one or moreaudio signals representative of background noise, which may beattenuated relative to the at least one conditioned audio signal. Forexample, processor 210 may be configured to transmit audio signalscorresponding to sounds 2020, 2021, and 2022. The signal associated with2020, however, may be amplified in relation to sounds 2021 and 2022based on a determination that sound 2020 emanates from within region2030. In some embodiments, hearing interface device 1810 may include aspeaker associated with an earpiece. For example, hearing interfacedevice 1810 may be inserted at least partially into the ear of the userfor providing audio to the user. Hearing interface device may also beexternal to the ear, such as a behind-the-ear hearing device, one ormore headphones, a small portable speaker, or the like. In someembodiments, hearing interface device may include a bone conductionmicrophone 1811, configured to provide an audio signal to user throughvibrations of a bone of the user's head. Such devices may be placed incontact with the exterior of the user's skin, or may be implantedsurgically and attached to the bone of the user.

In addition to recognizing voices of individuals speaking to user 100,the systems and methods described above may also be used to recognizethe voice of user 100. For example, voice recognition unit 2341 may beconfigured to analyze audio signals representative of sounds collectedfrom the user's environment to recognize the voice of user 100. Similarto the selective conditioning of the voice of recognized individuals,the voice of user 100 may be selectively conditioned. For example,sounds may be collected by microphone 1920, or by a microphone ofanother device, such as a mobile phone (or a device linked to a mobilephone). Audio signals corresponding to the voice of user 100 may beselectively transmitted to a remote device, for example, by amplifyingthe voice of user 100 and/or attenuating or eliminating altogethersounds other than the user's voice. Accordingly, a voice print of one ormore users of apparatus 110 may be collected and/or stored to facilitatedetection and/or isolation of the user's voice, as described in furtherdetail above.

FIG. 25 is a flowchart showing an exemplary process 2500 for selectivelytransmitting audio signals associated with a voice of a recognized userconsistent with disclosed embodiments. Process 2500 may be performed byone or more processors associated with apparatus 110, such as processor210.

In step 2510, process 2500 may include receiving audio signalsrepresentative of sounds captured by a microphone. For example,apparatus 110 may receive audio signals representative of sounds 2020,2021, and 2022, captured by microphone 1920. Accordingly, the microphonemay include a directional microphone, a microphone array, a multi-portmicrophone, or various other types of microphones, as described above.In step 2512, process 2500 may include identifying, based on analysis ofthe received audio signals, one or more voice audio signalsrepresentative of a recognized voice of the user (or anotherindividual). For example, the voice of the user may be recognized basedon a voice print associated with the user, which may be stored in memory550, database 2350, or other suitable locations. Processor 210 mayrecognize the voice of the user (or another individual), for example,using voice recognition component 2341. Processor 210 may separate anongoing voice signal associated with the user almost in real time, e.g.,with a minimal delay, using a sliding time window. The voice may beseparated by extracting spectral features of an audio signal accordingto the methods described above.

In step 2514, process 2500 may include causing transmission, to aremotely located device, of the one or more voice audio signalsrepresentative of the recognized voice of the user (or anotherindividual). The remotely located device may be any device configured toreceive audio signals remotely, either by a wired or wireless form ofcommunication. In some embodiments, the remotely located device may beanother device of the user, such as a mobile phone, an audio interfacedevice, or another form of computing device. In some embodiments, thevoice audio signals may be processed by the remotely located deviceand/or transmitted further. In step 2516, process 2500 may includepreventing transmission, to the remotely located device, of at least onebackground noise audio signal different from the one or more voice audiosignals representative of a recognized voice of the user. For example,processor 210 may attenuate and/or eliminate audio signals associatedwith sounds 2020, 2021, or 2023, which may represent background noise.The voice of the user (or another individual) may be separated fromother noises using the audio processing techniques described above.

In an exemplary illustration, the voice audio signals may be captured bya headset or other device worn by the user. The voice of the user may berecognized and isolated from the background noise in the environment ofthe user. The headset may transmit the conditioned audio signal of theuser's voice to a mobile phone of the user. For example, the user may beon a telephone call and the conditioned audio signal may be transmittedby the mobile phone to a recipient of the call. The voice of the usermay also be recorded by the remotely located device. The audio signal,for example, may be stored on a remote server or other computing device.In some embodiments, the remotely located device may process thereceived audio signal, for example, to convert the recognized user'svoice into text.

Lip-Tracking Hearing Aid

Consistent with the disclosed embodiments, a hearing aid system mayselectively amplify audio signals based on tracked lip movements. Thehearing aid system analyzes captured images of the environment of a userto detect lips of an individual and track movement of the individual'slips. The tracked lip movements may serve as a cue for selectivelyamplifying audio received by the hearing aid system. For example, voicesignals determined to sync with the tracked lip movements or that areconsistent with the tracked lip movements may be selectively amplifiedor otherwise conditioned. Audio signals that are not associated with thedetected lip movement may be suppressed, attenuated, filtered or thelike.

User 100 may wear a hearing aid device consistent with the camera-basedhearing aid device discussed above. For example, the hearing aid devicemay be hearing interface device 1810, as shown in FIG. 18 . Hearinginterface device 1810 may be any device configured to provide audiblefeedback to user 100. Hearing interface device 1810 may be placed in oneor both ears of user 100, similar to traditional hearing interfacedevices. As discussed above, hearing interface device 1810 may be ofvarious styles, including in-the-canal, completely-in-canal, in-the-ear,behind-the-ear, on-the-ear, receiver-in-canal, open fit, or variousother styles. Hearing interface device 1810 may include one or morespeakers for providing audible feedback to user 100, microphones fordetecting sounds in the environment of user 100, internal electronics,processors, memories, etc. In some embodiments, in addition to orinstead of a microphone, hearing interface device 1810 may comprise oneor more communication units, and one or more receivers for receivingsignals from apparatus 110 and transferring the signals to user 100.Hearing interface device 1810 may correspond to feedback outputting unit230 or may be separate from feedback outputting unit 230 and may beconfigured to receive signals from feedback outputting unit 230.

In some embodiments, hearing interface device 1810 may comprise a boneconduction headphone 1811, as shown in FIG. 18 . Bone conductionheadphone 1811 may be surgically implanted and may provide audiblefeedback to user 100 through bone conduction of sound vibrations to theinner ear. Hearing interface device 1810 may also comprise one or moreheadphones (e.g., wireless headphones, over-ear headphones, etc.) or aportable speaker carried or worn by user 100. In some embodiments,hearing interface device 1810 may be integrated into other devices, suchas a Bluetooth™ headset of the user, glasses, a helmet (e.g., motorcyclehelmets, bicycle helmets, etc.), a hat, etc.

Hearing interface device 1810 may be configured to communicate with acamera device, such as apparatus 110. Such communication may be througha wired connection, or may be made wirelessly (e.g., using a Bluetooth™,NFC, or forms of wireless communication). As discussed above, apparatus110 may be worn by user 100 in various configurations, including beingphysically connected to a shirt, necklace, a belt, glasses, a wriststrap, a button, or other articles associated with user 100. In someembodiments, one or more additional devices may also be included, suchas computing device 120. Accordingly, one or more of the processes orfunctions described herein with respect to apparatus 110 or processor210 may be performed by computing device 120 and/or processor 540.

As discussed above, apparatus 110 may comprise at least one microphoneand at least one image capture device. Apparatus 110 may comprisemicrophone 1920, as described with respect to FIG. 19 . Microphone 1920may be configured to determine a directionality of sounds in theenvironment of user 100. For example, microphone 1920 may comprise oneor more directional microphones, a microphone array, a multi-portmicrophone, or the like. Processor 210 may be configured to distinguishsounds within the environment of user 100 and determine an approximatedirectionality of each sound. For example, using one or an array ofmicrophones 1920, processor 210 may compare the relative timing oramplitude of an individual sound among the microphones 1920 to determinea directionality relative to apparatus 100. Apparatus 110 may compriseone or more cameras, such as camera 1930, which may correspond to imagesensor 220. Camera 1930 may be configured to capture images of thesurrounding environment of user 100. Apparatus 110 may also use one ormore microphones of hearing interface device 1810 and, accordingly,references to microphone 1920 used herein may also refer to a microphoneon hearing interface device 1810.

Processor 210 (and/or processors 210 a and 210 b) may be configured todetect a mouth and/or lips associated with an individual within theenvironment of user 100. FIG. 26 shows an exemplary individual 2610 thatmay be captured by camera 1930 (see FIG. 19 ) in the environment of auser consistent with the present disclosure. As shown in FIG. 26 ,individual 2610 may be physically present with the environment of user100. Processor 210 may be configured to analyze images captured bycamera 1930 to detect a representation of individual 2610 in the images.Processor 210 may use a facial recognition component, such as facialrecognition component 2340, described above, to detect and identifyindividuals in the environment of user 100. Processor 210 may beconfigured to detect one or more facial features of user 2610, includinga mouth 2611 of individual 2610. Accordingly, processor 210 may use oneor more facial recognition and/or feature recognition techniques, asdescribed further below.

In some embodiments, processor 210 may detect a visual representation ofindividual 2610 from the environment of user 100, such as a video ofuser 2610. As shown in FIG. 27 , user 2610 may be detected on thedisplay of a display device 2601 (such as, for example, computing device120). Display device 2601 may be any device capable of displaying avisual representation of an individual. For example, display device maybe a personal computer, a laptop, a mobile phone, a tablet, atelevision, a movie screen, a handheld gaming device, a videoconferencing device (e.g., Facebook Portal™, etc.), a baby monitor, etc.The visual representation of individual 2610 may be a live video feed ofindividual 2610, such as a video call, a conference call, a surveillancevideo, etc. In other embodiments, the visual representation ofindividual 2610 may be a prerecorded video or image, such as a videomessage, a television program, or a movie. Processor 210 may detect oneor more facial features based on the visual representation of individual2610, including a mouth 2611 of individual 2610.

FIG. 28 illustrates an exemplary lip-tracking system consistent with thedisclosed embodiments. Processor 210 may be configured to detect one ormore facial features of individual 2610, which may include, but is notlimited to the individual's mouth 2611. Accordingly, processor 210 mayuse one or more image processing techniques to recognize facial featuresof the user, such as convolutional neural networks (CNN),scale-invariant feature transform (SIFT), histogram of orientedgradients (HOG) features, or other techniques. In some embodiments,processor 210 may be configured to detect one or more points 2820associated with the mouth 2611 of individual 2610. Points 2820 mayrepresent one or more characteristic points of an individual's mouth,such as one or more points along the individual's lips or the corner ofthe individual's mouth. The points 2820 shown in FIG. 28 are forillustrative purposes only and it is understood that any points fortracking the individual's lips may be determined or identified via oneor more image processing techniques. Points 2820 may be detected atvarious other locations, including points associated with theindividual's teeth, tongue, cheek, chin, eyes, etc. Processor 210 maydetermine one or more contours of mouth 2611 (e.g., represented by linesor polygons) based on points 2820 or based on the captured image. Thecontour may represent the entire mouth 2611 or may comprise multiplecontours, for example including a contour representing an upper lip anda contour representing a lower lip. Each lip may also be represented bymultiple contours, such as a contour for the upper edge and a contourfor the lower edge of each lip. Processor 210 may further use variousother techniques or characteristics, such as color, edge, shape, ormotion detection algorithms to identify the lips of individual 2610. Theidentified lips may be tracked over multiple frames or images. Processor210 may use one or more video tracking algorithms, such as mean-shifttracking, contour tracking (e.g., a condensation algorithm), or variousother techniques. Accordingly, processor 210 may be configured to trackmovement of the lips of individual 2610 in real time.

The tracked lip movement of individual 2610 may be used to separate ifrequired, and selectively condition one or more sounds in theenvironment of user 100. FIG. 29 is a schematic illustration showing anexemplary environment 2900 for use of a lip-tracking hearing aidconsistent with the present disclosure. Apparatus 110, worn by user 100may be configured to identify one or more individuals within environment2900. For example, apparatus 110 may be configured to capture one ormore images of the surrounding environment 2900 using camera 1930. Thecaptured images may include a representation of individuals 2610 and2910, who may be present in environment 2900. As explained previouslywith reference to FIG. 27 , the captured images may also include imagesof individual 2610 presented on a display device (live or prerecordedimages). Processor 210 may be configured to detect a mouth ofindividuals 2610 and 2910 and track their respective lip movements usingthe methods described above. In some embodiments, processor 210 mayfurther be configured to identify individuals 2610 and 2910, forexample, by detecting facial features of individuals 2610 and 2910 andcomparing them to a database, as discussed previously.

In addition to detecting images, apparatus 110 may be configured todetect one or more sounds in the environment of user 100. For example,microphone 1920 may detect one or more sounds 2921, 2922, and 2923within environment 2900. In some embodiments, the sounds may representvoices of various individuals. For example, as shown in FIG. 29 , sound2921 may represent a voice of individual 2610 and sound 2922 mayrepresent a voice of individual 2910. Sound 2923 may representadditional voices and/or background noise within environment 2900.Processor 210 may be configured to analyze sounds 2921, 2922, and 2923to separate and identify audio signals associated with voices. Forexample, processor 210 may use one or more speech or voice activitydetection (VAD) algorithms and/or the voice separation techniquesdescribed above. When there are multiple voices detected in theenvironment, processor 210 may isolate audio signals associated witheach voice. In some embodiments, processor 210 may perform furtheranalysis on the audio signal associated the detected voice activity torecognize the speech of the individual. For example, processor 210 mayuse one or more voice recognition algorithms (e.g., Hidden MarkovModels, Dynamic Time Warping, neural networks, or other techniques) torecognize the voice of the individual. Processor 210 may also beconfigured to recognize the words spoken by individual 2610 usingvarious speech-to-text algorithms. In some embodiments, instead of usingmicrophone 1920, apparatus 110 may receive audio signals from anotherdevice through a communication component, such as wireless transceiver530 (see FIG. 17A). For example, if user 100 is on a video call,apparatus 110 may receive an audio signal representing a voice of user2610 from display device 2601 (see FIG. 27 ) or another auxiliarydevice.

Processor 210 may determine, based on lip movements and the detectedsounds, which individuals in environment 2900 are speaking. For example,processor 210 may track lip movements associated with mouth 2611 todetermine that individual 2610 is speaking (see FIG. 28 ). A comparativeanalysis may be performed between the detected lip movement and thereceived audio signals. In some embodiments, processor 210 may determinethat individual 2610 is speaking based on a determination that mouth2611 is moving at the same time as sound 2921 is detected. For example,when the lips of individual 2610 stop moving, this may correspond with aperiod of silence or reduced volume in the audio signal associated withsound 2921. In some embodiments, processor 210 may be configured todetermine whether specific movements of mouth 2611 correspond to thereceived audio signal. For example, processor 210 may analyze thereceived audio signal to identify specific phonemes, phonemecombinations or words in the received audio signal. Processor 210 mayrecognize whether specific lip movements of mouth 2611 correspond to theidentified words or phonemes. Various machine learning or deep learningtechniques may be implemented to correlate the expected lip movements tothe detected audio. For example, a training data set of known sounds andcorresponding lip movements may be fed to a machine learning algorithmto develop a model for correlating detected sounds with expected lipmovements. Other data associated with apparatus 110 may further be usedin conjunction with the detected lip movement to determine and/or verifywhether individual 2610 is speaking, such as a look direction of user100 or individual 2610, a detected identity of user 2610, a recognizedvoice print of user 2610, etc.

Based on the detected lip movement, processor 210 may cause selectiveconditioning of audio associated with individual 2610. The conditioningmay include amplifying audio signals determined to correspond to sound2921 (which may correspond to a voice of individual 2610) relative toother audio signals. In some embodiments, amplification may beaccomplished digitally, for example by processing audio signalsassociated with sound 2921 relative to other signals. Additionally, oralternatively, amplification may be accomplished by changing one or moreparameters of microphone 1920 to focus on audio sounds associated withindividual 2610. For example, microphone 1920 may be a directionalmicrophone and processor 210 may perform an operation to focusmicrophone 1920 on sound 2921. Various other techniques for amplifyingsound 2921 may be used, such as using a beamforming microphone array,acoustic telescope techniques, etc. The conditioned audio signal may betransmitted to hearing interface device 1810, and thus may provide user100 with audio conditioned based on the individual who is speaking.

In some embodiments, selective conditioning may include attenuation orsuppressing one or more audio signals not associated with individual2610, such as sounds 2922 and 2923. Similar to amplification of sound2921, attenuation of sounds may occur through processing audio signals,or by varying one or more parameters associated with microphone 1920 todirect focus away from sounds not associated with individual 2610.

In some embodiments, conditioning may further include changing a tone ofone or more audio signals corresponding to sound 2921 to make the soundmore perceptible to user 100. For example, user 100 may have lessersensitivity to tones in a certain range and conditioning of the audiosignals may adjust the pitch of sound 2921. For example, user 100 mayexperience hearing loss in frequencies above 10 kHz and processor 210may remap higher frequencies (e.g., at 15 kHz) to 10 kHz. In someembodiments processor 210 may be configured to change a rate of speechassociated with one or more audio signals. Processor 210 may beconfigured to vary the rate of speech of individual 2610 to make thedetected speech more perceptible to user 100. If speech recognition hasbeen performed on the audio signal associated with sound 2921,conditioning may further include modifying the audio signal based on thedetected speech. For example, processor 210 may introduce pauses orincrease the duration of pauses between words and/or sentences, whichmay make the speech easier to understand. Various other processing maybe performed, such as modifying the tone of sound 2921 to maintain thesame pitch as the original audio signal, or to reduce noise within theaudio signal.

The conditioned audio signal may then be transmitted to hearinginterface device 1810 and then produced for user 100. Thus, in theconditioned audio signal, sound 2921 (may be louder and/or more easilydistinguishable than sounds 2922 and 2923. It is also contemplated that,in some embodiments, the conditioned audio signal may be transcribed anddisplayed as text (e.g., on display device of computing device 120).

Processor 210 may be configured to selectively condition multiple audiosignals based on which individuals associated with the audio signals arecurrently speaking. For example, individual 2610 and individual 2910 maybe engaged in a conversation within environment 2900 and processor 210may be configured to transition from conditioning of audio signalsassociated with sound 2921 to conditioning of audio signals associatedwith sound 2922 based on the respective lip movements of individuals2610 and 2910. For example, lip movements of individual 2610 mayindicate that individual 2610 has stopped speaking or lip movementsassociated with individual 2910 may indicate that individual 2910 hasstarted speaking. Accordingly, processor 210 may transition betweenselectively conditioning audio signals associated with sound 2921 toaudio signals associated with sound 2922. In some embodiments, processor210 may be configured to process and/or condition both audio signalsconcurrently but only selectively transmit the conditioned audio tohearing interface device 1810 based on which individual is speaking.Where speech recognition is implemented, processor 210 may determineand/or anticipate a transition between speakers based on the context ofthe speech. For example, processor 210 may analyze audio signalsassociate with sound 2921 to determine that individual 2610 has reachedthe end of a sentence or has asked a question, which may indicateindividual 2610 has finished or is about to finish speaking.

In some embodiments, processor 210 may be configured to select betweenmultiple active speakers to selectively condition audio signals. Forexample, individuals 2610 and 2910 may both be speaking at the same timeor their speech may overlap during a conversation. Processor 210 mayselectively condition audio associated with one speaking individualrelative to others. This may include giving priority to a speaker whohas started but not finished a word or sentence or has not finishedspeaking altogether when the other speaker started speaking. Thisdetermination may also be driven by the context of the speech, asdescribed above.

Various other factors may also be considered in selecting among activespeakers. For example, a look direction of the user may be determinedand the individual in the look direction of the user may be given higherpriority among the active speakers. Priority may also be assigned basedon the look direction of the speakers. For example, if individual 2610is looking at user 100 and individual 2910 is looking elsewhere, audiosignals associated with individual 2610 may be selectively conditioned.In some embodiments, priority may be assigned based on the relativebehavior of other individuals in environment 2900. For example, if bothindividual 2610 and individual 2910 are speaking and more otherindividuals are looking at individual 2910 than individual 2610, audiosignals associated with individual 2910 may be selectively conditionedover those associated with individual 2610. In embodiments where theidentity of the individuals is determined, priority may be assignedbased on the relative status of the speakers, as discussed previously ingreater detail. User 100 may also provide input into which speakers areprioritized through predefined settings or by actively selecting whichspeaker to focus on.

Processor 210 may also assign priority based on how the representationof individual 2610 is detected. While individuals 2610 and 2910 areshown to be physically present in environment 2900, one or moreindividuals may be detected as visual representations of the individual(e.g., on a display device) as shown in FIG. 27 . Processor 210 mayprioritize speakers based on whether or not they are physically presentin environment 2900. For example, processor 210 may prioritize speakerswho are physically present over speakers displayed on a display device(see FIG. 27 ). Alternatively, processor 210 may prioritize a video overspeakers in a room, for example, if user 100 is on a video conference orif user 100 is watching a movie. The prioritized speaker or speaker type(e.g., present or not) may also be indicated by user 100, using a userinterface associated with apparatus 110.

FIG. 30 is a flowchart showing an exemplary process 3000 for selectivelyamplifying audio signals based on tracked lip movements consistent withdisclosed embodiments. Process 3000 may be performed by one or moreprocessors associated with apparatus 110, such as processor 210. Theprocessor(s) may be included in the same common housing as microphone1920 and camera 1930 used in process 3000 (see, for example, see FIGS.17A and 23 where image sensor 220 and microphone 1710 are incorporatedin the housing of apparatus 110). In some embodiments, some or all ofprocess 3000 may be performed on processors external to apparatus 110,which may be included in a second housing. For example, one or moreportions of process 3000 may be performed by processors in hearinginterface device 1810, or in an auxiliary device, such as computingdevice 120 or display device 2601 (e.g., computing device 120, see FIG.17C). In such embodiments, the processor may be configured to receivethe captured images via a wireless link between a transmitter in thecommon housing and receiver in the second housing.

In step 3010, process 3000 may include receiving a plurality of imagescaptured by a wearable camera from an environment of the user. Theimages may be captured by a wearable camera such as camera 1930 ofapparatus 110 (or image sensor 220 of FIG. 23 , etc.). In step 3020,process 3000 may include identifying a representation of at least oneindividual in at least one of the plurality of images. The individualmay be physically present in the environment of the user or may bedisplayed (e.g., on a display device) in the environment of the user.The individual may be identified using various image detectionalgorithms, such as Haar cascade, histograms of oriented gradients(HOG), deep convolution neural networks (CNN), scale-invariant featuretransform (SIFT), or the like. In some embodiments, processor 210 may beconfigured to detect visual representations of individuals, for examplefrom a display device, as shown in FIG. 27 .

In step 3030, process 3000 may include identifying at least one lipmovement or lip position associated with a mouth of the individual,based on analysis of the plurality of images. Processor 210 may beconfigured to identify one or more points associated with the mouth ofthe individual. In some embodiments, processor 210 may develop a contourassociated with the mouth of the individual, which may define a boundaryassociated with the mouth or lips of the individual. The lips identifiedin the image may be tracked over multiple frames or images to identifythe lip movement. Accordingly, processor 210 may use various videotracking algorithms, as described above. It is also contemplated that,in some embodiments, in addition to or as an alternative to tracking themovement of the mouth, processor 210 may use the plurality of images totrack the movement of other facial features of the individual to detectspeech.

In step 3040, process 3000 may include receiving audio signalsrepresentative of the sounds captured by a microphone 1920 from theenvironment of the user. For example, apparatus 110 may receive audiosignals representative of sounds 2921, 2922, and 2923 captured bymicrophone 1920. In step 3050, process 3000 may include identifying,based on analysis of the sounds captured by the microphone, a firstaudio signal associated with a first voice and a second audio signalassociated with a second voice different from the first voice. Forexample, processor 210 may identify an audio signal associated withsounds 2921 and 2922, representing the voice of individuals 2610 and2910, respectively. Processor 210 may analyze the sounds received frommicrophone 1920 to separate the first and second voices using anycurrently known or future developed techniques or algorithms. Step 3050may also include identifying additional sounds, such as sound 2923 whichmay include additional voices or background noise in the environment ofthe user. In some embodiments, processor 210 may perform furtheranalysis on the first and second audio signals, for example, bydetermining the identity of individuals 2610 and 2910 using availablevoice prints thereof. Alternatively, or additionally, processor 210 mayuse speech recognition tools or algorithms to recognize the speech ofthe individuals.

In step 3060, process 3000 may include causing selective conditioning ofthe first audio signal based on a determination that the first audiosignal is associated with the identified lip movement associated withthe mouth of the individual. Processor 210 may compare the identifiedlip movement with the first and second audio signals identified in step3050. For example, processor 210 may compare the timing of the detectedlip movements with the timing of the voice patterns in the audiosignals. In embodiments where speech is detected, processor 210 mayfurther compare specific lip movements to phonemes or other featuresdetected in the audio signal, as described above. Accordingly, processor210 may determine that the first audio signal is associated with thedetected lip movements and is thus associated with an individual who isspeaking.

Various forms of selective conditioning may be performed, as discussedabove. In some embodiments, conditioning may include changing the toneor playback speed of an audio signal. For example, conditioning mayinclude remapping the audio frequencies or changing a rate of speechassociated with the audio signal. In some embodiments, the conditioningmay include amplification of a first audio signal relative to otheraudio signals. Amplification may be performed by various means, such asoperation of a directional microphone, varying one or more parametersassociated with the microphone, or digitally processing the audiosignals. The conditioning may include attenuating or suppressing one ormore audio signals that are not associated with the detected lipmovement. The attenuated audio signals may include audio signalsassociated with other sounds detected in the environment of the user,including other voices such as a second audio signal. For example,processor 210 may selectively attenuate the second audio signal based ona determination that the second audio signal is not associated with theidentified lip movement associated with the mouth of the individual. Insome embodiments, the processor may be configured to transition fromconditioning of audio signals associated with a first individual toconditioning of audio signals associated with a second individual whenidentified lip movements of the first individual indicates that thefirst individual has finished a sentence or has finished speaking.

In step 3070, process 3000 may include causing transmission of theselectively conditioned first audio signal to a hearing interface deviceconfigured to provide sound to an ear of the user. The conditioned audiosignal, for example, may be transmitted to hearing interface device1810, which may provide sound corresponding to the first audio signal touser 100. Additional sounds such as the second audio signal may also betransmitted. For example, processor 210 may be configured to transmitaudio signals corresponding to sounds 2921, 2922, and 2923. The firstaudio signal, which may be associated with the detected lip movement ofindividual 2610, may be amplified, however, in relation to sounds 2922and 2923 as described above. In some embodiments, hearing interface 1810device may include a speaker associated with an earpiece. For example,hearing interface device may be inserted at least partially into the earof the user for providing audio to the user. Hearing interface devicemay also be external to the ear, such as a behind-the-ear hearingdevice, one or more headphones, a small portable speaker, or the like.In some embodiments, hearing interface device may include a boneconduction microphone, configured to provide an audio signal to userthrough vibrations of a bone of the user's head. Such devices may beplaced in contact with the exterior of the user's skin, or may beimplanted surgically and attached to the bone of the user.

Using Audio and Images for Speech Diarization

Multiple possibilities are enabled by apparatus 110 combining a soundcapture device and an image capture device such as, for example,cataloging or diarization of audio captured by the device. Asschematically illustrated in FIG. 31A, diarization is the process ofpartitioning an input audio stream into segments according to thespeaker's identity. For example, speaker diarization may involvepartitioning an input audio signal stream 3100 into multiple audiosegments 3100A, 3100B, 3100C, 3100D according to the speaker's identity.Diarization may enhance the readability of automatic speechtranscription by structuring the audio stream 3100 into audio segments3100A, 3100B, 3100C, 3100D associated with different speakers.Diarization, when used together with speaker recognition systems, mayenable identification of the speakers even when the speaker is notvisible. Such processes may assist in identifying not only the speaker,but also the words spoken by a particular speaker. Speaker diarizationmay also involve identifying points in an audio stream when a speakerchanges. After an audio stream is partitioned into different segmentsbased on different speakers, speech associated with each speaker may beclustered or grouped and catalogued together in a database.

Diarization of the audio captured by apparatus 110 can be performed bycombining the audio and video (or images) captured by apparatus 110.Tracking the active speaker in the captured images, for example, bytheir mouth movements and facial gestures may enable apparatus 110 todifferentiate parts of the audio signal which are associated withdifferent speakers. Speaker changes in an audio signal stream 3100 maybe detected in various ways. In some embodiments, speaker changes may bedetected by the user changing the looking direction (e.g., the user gazechanging from a first speaker to a second speaker). For example, basedon a detected change in user look direction 1850 (see, FIG. 20 ), aprocessor associated with apparatus 110 may detect that the user is nowlooking at a different individual (e.g., a second individual), and ifthe tracked lip movements (or changes in other facial features of thesecond individual) indicate that the second individual is speaking, theprocessor may associate the audio signal received at that time with thesecond individual. In some embodiments, a processor associated withapparatus 110 may detect that the speaker has changed based on lipmovements in the captured images, etc. For example, as described withreference to FIGS. 26-28 , facial features (lip position, etc.) in theimages received concurrent with the audio signal may indicate that thespeaker has changed. The speech associated with the different speakersmay be cataloged or recorded in a database (e.g., database 2350 of FIG.23 ) accessible to apparatus 110 (e.g., memory 550, 550 a, 550 b, etc.,see FIGS. 17A-17C, 23 ). Thus, the processor may identify parts of theaudio signal associated with different speakers captured in the image.

In some embodiments, segments of the audio signal in which two or morespeakers are speaking simultaneously may be ignored and/or discarded. Insome embodiments, this segment of the audio signal may also be cataloged(e.g., for later clarification). In some embodiments, diarization may bedone with minimal processing of the input audio signal. In someembodiments, the audio signals may be processed (e.g., filtered, etc.)prior to, or during, diarization. Processing of the audio signals may beuseful in improving accuracy of the diarization. Having accuratediarization may be used for generating a high-quality voice signature(or voice print) of each speaker and associating the speaker's imagewith the user's voice signature when the speaker's image is alsoavailable. The speaker's image 3110 and voice print 3120 may beassociated with each other and stored in database 2350. In someembodiments, in addition to the speaker's image 3110 and voice print3120, a transcript 3140 of the conversation (e.g., the completeconversation, portions of the conversation, keywords uttered, etc.),and/or additional details 3150 (e.g., name, address, birthday, names ofspouse/children, speaker's association with the user, etc.) of thespeaker may also be stored in the database 2350.

If information (e.g., images 3110, voice print 3120, or other data)related to the speaker has been previously stored in database 2350, theinformation may be revised or supplemented. The speaker may be known tothe user or not. In some embodiments, if the speaker's voice print 3120is previously stored in database 2350, the prior voice print may bereplaced, updated, enhanced, etc. using a newly acquired voice print. Insome embodiments, a grade or score may be associated with each acquired(and stored) voice print based, for example, on its quality. The grademay be generated, for example, based on one or more of the quality(clarity, pitch, frequency, noise level, etc.) of the audio signal uponwhich the voice print was constructed, the length of the audio,additional audio signal parameters, etc. In general, any known techniqueto grade audio signals may be used to grade the audio signal based uponwhich the voice print is generated. For example, in some embodiments,the International Telecommunications Union (ITU) standard for audioquality (BS.1387), commonly referred to as perceptual evaluation ofaudio quality (PEAQ), may be used to grade the audio signals. If a newlyavailable voice print is of higher quality that a previously storedvoice print 3120, the previously stored voice print 3120 may be replacedwith the newly generated voice print. In some embodiments, if at leastone attribute (or a selected number of attributes) of the newlyavailable voice print is better in quality than the previously storedvoice print 3120, the previously stored voice print 3120 may be replacedwith the newly generated voice print. In some embodiments, the newlygenerated voice print may be used to enhance or enrich (e.g., increasethe length, etc.) the previously stored voice print 3120. Similarly, ifno image of the speaker's is stored in database 2350, or a currentlycaptured image is better in quality than the stored image 3110 (e.g., ofhigher resolution, sharper, better contrast, clearer relative to thebackground, etc.), the new image may be stored in database 2350 insteadof, or in addition to, the previously stored image 3110. In someembodiments, if at least one attribute (or a selected number ofattributes) of the new image is better in quality than the previouslystored image 3110, the previously stored image 3110 may be replaced withthe new image.

When the user of apparatus 110 meets a speaker (any individual), if nodata related to the speaker is stored in database 2350, a new entry maybe created and associated with the speaker. The entry may include thespeaker's image 3110 and voice print 3120. In some embodiments, atranscript 3140 of the speaker's speech may also be stored in database2350 in association with the speaker's image 3110 and voice print 3120.In some embodiments, other relevant details 3150 of the speaker and/orthe meeting may also be included in database 2350. For example, theplace or location (e.g., address, etc.) of the meeting, speaker's nameand other details of the speaker (if known), such as, for example,address, employer, memberships, associations with the user (details ofprior meetings, etc.), etc., may also be included. This information maybe generated in various ways. For example, the speaker may haveintroduced himself, the user (or other individuals) may have addressedthe speaker, an image of the name tag of the speaker may have beencaptured by image sensor and recognized, etc. In some embodiments, basedon the name (or other information), additional relevant details 3150 ofthe speaker may be obtained by apparatus 110 (or another deviceassociated with apparatus 110, such as, for example, computing device120, computer server 250, etc.) from, for example, social network sites.Any data related to the speaker's speech may be stored as transcript3140 in database 2350. In some embodiments, the transcript 3140 storedin database 2350 in association with the speaker's image 3110 and voiceprint 3120 may be digital transcript of the entire conversation,portions of the conversation (e.g., a selected time before and/or aftera keyword is uttered, etc.), spoken keywords, discussed topics, numberof times a predefined keyword is spoken, etc. In some embodiments, atime stamp 3130 (e.g., date and/or time window when the conversation wasrecorded, etc.) may also be included in the database 2350.

When the speaker subsequently encounters the user (e.g., a secondencounter in future), based on a comparison of the voice print of thespeaker's speech recorded during the second encounter with the voiceprints 3120 stored in database 2350, the system may recognize thespeaker. The speaker's voice print 3120 may have been previously storedin database 2350 (e.g., from a prior encounter). If the recorded voiceprint at the second encounter matches a voice print 3120 stored indatabase 2350, the system (i.e., a processor associated with apparatus110) may identify the speaker. Additionally, or alternatively, in someembodiments, as previously described with reference to FIG. 23 , thespeaker may be recognized based on a comparison of the speaker's imagerecorded during the second encounter with the images 3110 stored in thedatabase 2350. That is, if the recorded image of the speaker from thesecond encounter matches an image 3110 stored in database 2350, thesystem (e.g., a processor associated with apparatus 110) may identifythe speaker. In some embodiments, the stored image (of the speaker) maybe retrieved and displayed to the user (e.g., in a display device ofcomputing device 120). Accordingly, the user may recognize the speakerafter viewing the displayed image. For example, in embodiments where animage of the speaker is not available during the second encounter (e.g.,speaker is not visible to apparatus 110, etc.), the speaker may berecognized by comparing the speaker's voice print with those stored inmemory. Thus, in some embodiments, apparatus 110 may be configured toidentify the speaker based on the received audio signals.

In some embodiments, as described with reference to FIGS. 26-28 , imagesof the speaker received with the audio signal may be analyzed todetermine the speaker. That is, based on an analysis of the images(e.g., tracking lip movements, etc.) of multiple individuals capturedduring the second encounter, the processor (associated with apparatus110) may determine that the received audio signals correspond to thespeech of a particular individual. That is, the processor may detect whoamong the multiple individuals that user is meeting with at the sametime is the speaker at any particular time. The processor may thenextract the voice print from the received audio signal associated withthe speaker and compare the extracted voice print to the voice prints3120 stored in the database 2350 to determine the identity of thespeaker. As explained previously, in some embodiments, the processor maycompare the recorded image of the speaker with the images 3110 stored indatabase 2350 to determine the identity of the speaker. The processormay also record a transcript 3140 of the speaker's speech during thesecond encounter in the database 2350. The transcript 3140 may includethe entire conversation, parts of the conversation, a segment of theconversation associated with a keyword or keywords spoken, etc. In someembodiments, the processor may display the image of the speaker (e.g.,image retrieved from the database 2350) to the user, for example, via adisplay device of computing device 120 (e.g., a mobile phone paired withapparatus 110). Displaying the speaker's image to the user may help theuser in identifying the identity of the speaker when the speaker is notvisible to the speaker. If an image is not associated with the storedvoice print (of the speaker), other relevant details 3150 (name,characteristics, etc.) associated with the voice print may be displayedto the user.

FIG. 32A is a flowchart of a process or a method 3200 for processingaudio and video, in accordance with some embodiments of the disclosure.Method 3200 may be performed by one or more processors associated withapparatus 110, such as, for example, processor 210 of apparatus 110,processor 540 of computing device 210 (see FIG. 17C), etc. In someembodiments, some or all of method 3200 may be performed on processorsexternal to apparatus 110. In other words, the processor performingmethod 3200 may be included in the housing of apparatus 110 (i.e., samecommon housing as microphone and camera/image sensor) and/or may beincluded in a second housing (e.g., in housing of computing device 210).As explained previously, apparatus 110 may be a wearable device with oneor more image sensors 220 (camera 1930, etc.) and one or moremicrophones 1710 (1920, 1921, 1923 of FIG. 19 , etc.). When a user ismeeting with or interacting with other individuals in an environment(see, for e.g., FIG. 29 ), the image sensor(s) 220 of apparatus 110 wornby the user may capture a plurality of images from the user'senvironment and the microphone(s) 1710 captures sound from the user'senvironment.

In step 3210, the processor (e.g., associated with apparatus 110)receives audio signals captured by the microphone(s) of apparatus 110.The received audio signals in step 3210 may include the signalscorresponding to the sounds produced by different individuals. Forexample, the received audio signals may include segments (consecutive ornon-consecutive segments) where different individuals are speaking. Instep 3220, the processor receives images captured by apparatus 110(e.g., captured by image sensor 220 of apparatus 110). The receivedimages may include images of at least some of the individuals whosesounds were received in step 3210. In step 3230, the processor diarizesthe audio signals received in step 3210 using the received images instep 3220. As schematically illustrated in FIG. 31A, the diarization instep 3230 may involve partitioning the audio signals received in step3210 into multiple audio segments such that audio segments correspondingto the speech (or sounds) of one speaker are clustered, markedaccordingly, or otherwise associated. In step 3230, speaker changes inthe audio signals received in step 3210 are detected based on the imagesreceived in step 3220. In some embodiments, the speaker changes may bedetected based on analyzing the received images to determine that thelooking direction of the user of the apparatus has changed (e.g., theuser's gaze changed from looking toward a first speaker to a secondspeaker). For example, based on the image received, the processor maydetect that the user is now looking at a different individual andassociate the audio signals received at that time to that individual. Insome embodiments, the processor may detect speaker changes based onfacial features of individuals in the received images. For example, asdescribed with reference to FIGS. 26-28 , the position of the mouth,lips, chin, or other features of the individuals captured in the imagesmay indicate who the speaker at any time is and the processor mayassociate the audio signal received at that time with that individual.In step 3240, the processor may store the diarized audio signals in step3230 in a database accessible to apparatus 110. That is, the processormay associate the audio signal segments corresponding to differentspeakers with their respective images and store them in a databaseaccessible to apparatus 110 (e.g., database 2350 of FIG. 31B).

FIG. 32B is a flowchart of another process or method 3250 for processingaudio and video, in accordance with some embodiments of the disclosure.Like method 3200, method 3250 may also be performed by one or moreprocessors associated with apparatus 110. In step 3260, the processorreceives audio signals from apparatus 110. The received audio signalsmay include the signals corresponding to the sounds (e.g., speech)produced by at least two different individuals. In step 3270, theprocessor receives images from apparatus 110. The received images may atleast include a first image with a representation of one of the twoindividuals (first individual) whose sound was received in step 3260 anda second image with a representation of the other individual (secondindividual). In step 3280, the processor may use the first image toobtain a first audio signal segment corresponding to the sounds producedby the first individual. In step 3290, the processor may use the secondimage to obtain a second audio signal segment corresponding to soundsproduced by the second individual. As described with reference to method3200, the audio signal may be partitioned to the first and second audiosignal segment based on the first and second images. For example, basedon user look direction, positions or representations of facial featuresin the first and second images, etc. In some embodiments, the processormay then store the first and second audio signal segments in associationwith the first and second images in database 2350.

FIG. 33 is a flowchart of another process or method 3300 for processingaudio and video, in accordance with some embodiments of the disclosure.Like methods 3200 and 3250, method 3330 may also be performed by one ormore processors associated with apparatus 110. In step 3302, theprocessor receives one or more images from apparatus 110. One or moreimages of the received images may include an image of an individual fromthe environment of the user. In some embodiments, the individual (e.g.,individuals 2610, 2910) may be physically present in the user'senvironment. It is also contemplated that, in some embodiments, one ormore of the individuals may not be physically present in the user'senvironment. In step 3304, the processor may analyze one or more of theimages received to determine (e.g., based on lip movements, user lookdirection, etc.) the individual who is speaking. In step 3306, theprocessor receives the audio signals captured by apparatus 110 (e.g.,the microphone(s) of apparatus). The received audio signals may includethe signals corresponding to the sounds produced by the individual whois speaking. In step 3308, the processor may obtain an audio signalsegment from the received audio signals. This audio signal segment mayinclude signals corresponding to speech of the individual who isspeaking at that time. Obtaining the audio signal segment may includeseparating a portion of audio signals from a received audio signalstream. In this step, the processor may differentiate the portion of theaudio signals in which the individual is speaking from other portions ofthe audio signal to separate the audio signal segment. The processor maydifferentiate the portion of the audio signals based on an analysis ofthe one or more or the images received in step 3302. For example, theprocessor may detect who is speaking at a time, based on facial features(e.g., lip movement, lip position, etc.) of the individuals captured inthe received images. In step 3310, the processor may extract a voiceprint or voice signature of the individual from the audio signalsegment. In some embodiments, extracting the voice print may includeextracting one or more characteristics of the voice (or speech)contained in the audio signal segment. The characteristic may include,for example, pitch, tone, rate of speech, volume, rhythm, tempo,texture, resonance, center frequency, frequency distribution,responsiveness, etc. In step 3312, the processor may store the voiceprint of the speaker in association with the image of the speaker indatabase 2350.

Steps 3302-3312 may be repeated to store the voice prints and images ofmultiple individuals in the database 2350. For example, the processormay obtain a second audio segment from the received audio signals instep 3306. The second audio segment may include a portion of audiosignals in which a second individual is speaking. The processor may thenextract a voice print of the second individual from the second audiosegment and store it in association with an image of the secondindividual in the database 2350. The image of the second individual maybe one or the images received from apparatus 110 in step 3302. In someembodiments, the second individual's image (and voice print) may alreadybe stored in the database 2350 from a previous encounter. In some suchembodiments, the processor may compare the voice print of the secondindividual to the voice prints 3120 stored in the database 2350 toidentify the speaker and store the extracted voice print in the database2350 in association with the image of the second individual. If a voiceprint is previously stored in the database 2350 in association with thesecond individual's image, the processor may compare the newly extractedvoice print with the previously stored voice print to determine if atleast one attribute (e.g., clarity, tone, pitch, frequency range, etc.)of the extracted voice print is better in quality than the sameattribute of the stored voice print. If at least one attribute (e.g.,clarity, tone, pitch, frequency range, etc.) of the extracted voiceprint is better in quality than the same attribute of the stored voiceprint, the processor may replace the previously stored voice print withthe newly extracted voice print. In some embodiments, the processor maycompare the newly received image of the second individual to the imagepreviously stored in the database 2350 and replace the stored image withthe newly received image if its quality is better. Thus, in method 3300the voice prints 3120 of speakers may be associated with images 3110depicting those speakers and stored or catalogued in database 2350. Asexplained previously, in some embodiments, in addition to the speaker'simage 3110 and voice print 3120, a transcript 3140 of the speaker'sspeech, and/or other relevant details 3150 of the speaker may also bestored in database 2350 (see FIG. 31B).

Based on the associated voice prints and images stored in the database2350, segments of an audio signal received when a user engaged withindividuals may be diarized and the user's interaction (e.g.,conversation) with these individuals recorded and cataloged. Forexample, in an exemplary scenario, a user wearing apparatus 110 meetswith two individuals (e.g., first and second individuals) during ameeting. During the meeting, the first individual talks for the first 2minutes (10:00-10:02); the second individual talks for the next minute(10:02-10:03); the first individual talks again for the next threeminutes (10:03-10:06); and the second individual talks again for thenext two minutes (10:06-10:08). The microphone(s) of apparatus 110records the conversation and the image sensor of apparatus 110 capturesimages from the user's meeting with these individuals. The processorassociated with apparatus 110 receives audio signals corresponding tothe recorded conversation and the captured images. Based on receivedimages, the processor partitions the audio signals into segmentscorresponding to each individual's speech. That is, the processorpartitions the received audio signals as: a first audio segmentcorresponding to the first individual's speech from 10:00-10:02; asecond audio segment corresponding to the second individual's speechfrom 10:02-10:03; a third audio segment corresponding to the firstindividual's speech from 10:03-10:06; and a fourth audio segmentcorresponding to the second individual's speech from 10:06-10:08. Basedon a comparison of the extracted voice print of the audio segments withthe voice prints stored in database 2350 and/or by comparing theindividuals images with those stored in the database 2350, the processormay recognize the two individuals and associate the partitioned audiosegments with the respective speaker and store the audio segments (or atranscript 3140 of the audio segments) in database 2350 in associationwith the speaker (e.g., image 3110 and voice print 3120 of the speaker).That is, the conversation between the time windows 10:00-10:02 and10:03-10:06 may be stored in association with the first individual andthe conversation between the time windows 10:02-10:03 and 10:06-10:08may be stored in association with the second individual.

In some embodiments, the processor may be configured to receiveadditional audio signals (received at any time (e.g., hours, days, etc.)after the audio signals received in step 3306) representative ofadditional sounds captured by the microphone(s) of apparatus 110. Theprocessor may then obtain a subsequent audio segment that includes aportion of the additional audio signals in which a person is speakingfrom the additional audio signals. The processor may then extract avoice print from the subsequent audio segment and compare this voiceprint to one or more voice prints stored in the database 2350. Theprocessor may determine that the person is a particular individual whenat least one attribute (e.g., tone, pitch, etc.) of the voice printmatches an attribute of the voice print of that individual store indatabase 2350. In some embodiments, when the new voice print matches thevoice print of an individual stored in database 2350, the processor mayretrieve the image of that individual from the database 2350 anddisplayed it on a display device. In some embodiments, the processor maystore the received subsequent audio segment or one or more features(keywords, topics, etc.) extracted from the subsequent audio segment inthe database 2350 in association with the respective images and voiceprints.

In some embodiments, along with the additional audio signals, theprocessor may also receive a subsequent image (e.g., captured at thetime the subsequent audio signals was recorded) captured by the imagesensor of apparatus 110. The subsequent image may include arepresentation of a person. The processor may also obtain a subsequentaudio segment that includes a portion of the additional audio signals inwhich the person is speaking from the additional audio signals. Theprocessor may then compare the subsequent image with images stored inthe database 2350 to determine who that person is. The processor maydetermine that the person is a particular individual when therepresentation of the person in the subsequent image matches at leastone aspect (shape, features, color, hair style, etc.) of therepresentation of that individual in an image stored in database 2350.In some embodiments, the processor may replace the original image (ofthe individual) stored in the database 2350 with the newly receivedimage if at least one attribute (contrast, color, clarity, etc.) of thesubsequent image is better in quality than the same attribute of theimage stored in the database.

In some embodiments, the processor may receive audio signalsrepresentative of the sounds captured by the at least one microphone andmay receive a plurality of images captured by the image sensor. Theplurality of images may include a first image including a representationof a first individual and a second image including a representation of asecond individual. The processor may use the first image to obtain afirst audio segment from the audio signals and the second image toobtain a second audio segment from the audio signals. The first audiosegment may include a portion of the audio signals in which the firstindividual is speaking and the second audio segment includes a portionof the audio signals in which the second individual is speaking. Theprocessor may also receive a third image from the image sensor. Thethird image may also include a representation of the first individual.The processor may use the third image to obtain a third audio segment inwhich the first individual is speaking from the audio signals. Theprocessor may then associate (or diarize) the first and third audiosegments with the first individual and associate the second audiosegment with the second individual. In some embodiments, the processormay store the first and third audio segments (or transcripts of thefirst and third audio segments) in association with an identifier (e.g.,name, identification number, image, voice print, etc.) of the firstindividual in a database (e.g., database 2350 or another database) andthe second audio segment (or a transcript of the second audio segment)in association with an identifier of the second individual in thedatabase. It is also contemplated that, in some embodiments, theidentifier of the individuals may also include a username, letters,numbers, account number, device number, a combination of letters andnumbers, etc.

It is contemplated that the disclosure is not limited to twoindividuals, and is equally applicable to any number of individuals,wherein each individual may be associated with a plurality of audiosegments.

FIG. 34 is a flowchart of another process or method 3400 for processingaudio and video, in accordance with some embodiments of the disclosure.Similar to the previously described methods, method 3400 may also beperformed by one or more processors associated with apparatus 110. Instep 3404, the processor may receive one or more images captured byapparatus 110 (e.g., image sensor 220, camera 1930 of FIG. 19 , etc.).In step 3408, the processor may receive audio signals captured by anaudio sensor (e.g., microphones 1710 of FIG. 23 , microphones 1920,1921, 1923 of FIG. 19 , etc.) of apparatus 110. In step 3412, theprocessor may diarize the audio signal by, for example, clustering(e.g., associating or listing a new audio segment with a previous audiosegment from the same conversation) or creating another associationbetween audio segments of the audio signals received in step 3408 inwhich the same speaker is speaking. Step 3412 may be similar to, andinvolve similar operations as, step 3230 of method 3200 (of FIG. 32A).In this step, the processor may analyze the images to determine who isspeaking (e.g., based on lip movements of the speaker, changinglooking-direction the user, etc.) and associate an audio signal segment(e.g., a segment of the audio signals received at the same time thespeaker is speaking) with the speaker. The speaker may be identified byany of the techniques discussed previously, for example, based on achange in the looking direction of the user, by determining an activespeaker in a crowd of individuals based on lip movements, by identifyinga voice print, etc. In some embodiments, in step 3412, the audio signalsreceived in step 3408 may be diarized using the audio itself, forexample, by a classification algorithm. In some embodiments, in step3412, audio signals may be associated with multiple individuals in theuser's environment (for example, as discussed with reference to method3200 of FIG. 32A).

In step 3416, a voice print may be extracted from the audio segments ofone or more speakers. As explained previously, in some embodiments,extracting the voice print may include extracting one or morecharacteristics (e.g., pitch, tone, rate of speech, volume, rhythm,tempo, texture, resonance, center frequency, frequency distribution,responsiveness, etc.) of the voice contained in the audio segments. Instep 3420, the extracted voice print(s) may be stored in database 2350.Each voice print may be stored in association with an image of thespeaker. In some embodiments, the voice print may be associated with thespeaker's image and with additional details, a transcript of the audiosegment, keywords in the audio segment, topics extracted from the audiosegment (e.g., meeting date, etc.), details of the encounter such aslocation and time, the speaker's name and other details, etc. Aspreviously explained, if the speaker already has an entry in database2350, the entry may be updated, for example the voice print may beupdated or enhanced, the image may be added or may replace a previousimage, etc. The voice print and/or the image may be used to identifyfurther segments within the audio signal in which the same individualspeaks.

During a subsequent event (which may occur hours, days, weeks, months,or years after the first encounter), the user may meet the same speaker.During this future encounter, the image sensor(s) of apparatus 110 maycapture a plurality of images of the speaker and the microphone(s) ofapparatus 110 may capture audio signals corresponding to the speech ofthe speaker. In step 3424, the processor may receive the capturedimages, and in step 3428, the processor may receive the captured audio.In some embodiments, the speaker's image may not be included in theimages received in step 3424 (for example, because the speaker is not inthe field of view of the image sensor), and only the speaker's voice maybe captured by the audio sensor. In step 3432, after capturing a sample(e.g., audio signal of one second or more) of the speaker's voice, thecaptured voice may be compared against audio signals or voice printentries stored in database 2350, to identify the speaker. In someembodiments, in step 3432, a voice print of the speaker may be extractedfrom a segment of the received audio signals (in step 3428) where thespeaker is speaking and compared with the voice prints stored indatabase 2350 to identify the speaker.

Any known method (pattern recognition algorithms, etc.) may be used tocompare the recently-received audio signal (or voice print associatedwith the recently-received audio signals) with voice prints stored indatabase 2350 in step 3432. In some embodiments, a processor may breakdown the audio signals or voice prints into segments or individualsounds and analyze or compare each sound using algorithms (e.g., neuralnetworks, deep learning neural networks, etc.) to find if a match existsbetween the two audio signals (or voice prints). A match may bedetermined based on the relative degree of similarity between therecently-received audio signal (or voice print) and the stored audiosignal (or voice print). Any measure of closeness of two speech sounds(such as, for e.g., the Itakura-Saito measure, LPC cepstrum, etc.) maybe used to determine if a match exists between the recently-received andstored audio signals. In some embodiments, the processor may measure oneor more voice characteristics in the recently-received audio signal andcompare the measured characteristics to values stored in a database todetermine if a match exists. In some embodiments, a match may bedetermined to exist if a predetermined number of characteristics matchbetween the two audio signals or voice prints (recently-received andstored voice prints). In some embodiments, a match may be determined toexist if a pattern (e.g., voice pattern) in the recently-received audiosignal matches (or is within a predetermined range) a pattern in astored voice print.

Alternatively, or additionally, in some embodiments, in step 3432, theprocessor compares the image of the speaker received in step 3424 withthe images stored in database 2350 to identify the speaker using theimages. Any previously described facial recognition method (e.g.,described with reference to FIG. 23 ) or any other known method (patternrecognition algorithms, etc.) may be used to compare the received imagewith those stored in database 2350 to identify the speaker in step 3432.In some embodiments, if the quality of the received image is better thanthe stored image, the stored image may be replaced with the newlyreceived image.

In step 3436, a transcript of the received audio signals (in step 3428)corresponding to the speaker's speech may be stored in database 2350 inassociation with the speaker's voice print and image. In someembodiments, extracted portions of the speaker's speech may be stored indatabase 2350 in step 3436. For example, keywords uttered, segments ofthe conversation, topics discussed, follow-up items, etc., may be storedalong with prior entries. In some embodiments, in step 3436, the storedimage of the speaker (identified in step 3432) may be retrieved fromdatabase 2350 and displayed to the user. The image may be displayed onany display device associated with the user, for example, on a displaydevice of computing device 120 (e.g., a mobile phone, smart watch, PDA,laptop, etc.). Displaying the speaker's image may be particularlyhelpful when the user is unable to see the speaker such as, for example,if the speaker is out of the field of view of the user.

Additionally, or alternatively, in some embodiments, a textual oranother indication of relevant details (e.g., name, job title, companyassociation, etc.) of the speaker may be provided to the user on thedisplay device. It is also contemplated that, in some embodiments, anaudible indication of the relevant details of the speaker may also beprovided to the user on, for example, hearing interface device 1810 (seeFIG. 18 ). Any relevant details of the speaker (e.g., details of theuser's association with the speaker, keyword uttered or subjectsdiscussed during the previous encounter, etc.) may be displayed (orprovided) to the user. Additionally, or alternatively, the audio signalsreceived by processor may be processed, for example, to update the voiceprint, further speaker diarization, keyword extraction, topicextraction, etc.

FIG. 35 is a flowchart of another exemplary method 3500 for processingaudio and video, in accordance with some embodiments of the disclosure.Similar to the previously described methods, method 3500 may also beperformed by one or more processors associated with apparatus 110. Instep 3502, the processor may receive first audio signals captured by atleast one microphone (e.g., microphones 1710 of FIG. 23 , microphones1920, 1921, 1923 of FIG. 19 , etc.) of apparatus 110 at a first time.The first audio signals may include signals corresponding to speech (orother sounds) made by an individual (e.g., 2610, 2910 of FIG. 29 ) otherthan the user. In step 3504, the processor may extract a voice print ofthe individual from the first audio signals. In step 3506, the processormay receive one or more images captured by an image sensor (sensor 220,camera 1930 of FIG. 19 , etc.) of apparatus 110. One or more images ofthe received images may include an image of the individual whose soundis received in step 3502. In step 3508, the processor may analyze thereceived images to identify a representation of the individual who isspeaking (for e.g., using facial features as described with reference toFIGS. 26-28 ). In step 3510, the processor may cause the extracted voiceprint of the individual (in step 3504) to be stored in association withthe image of the individual in database 2350 or with multiple segmentsof the received first audio signals. In step 3512, the processor mayreceive second audio signals from the at least one microphone at asecond time after the first time. The second audio signals may includesignals corresponding to sounds made by a person other than the user. Instep 3514, the processor may determine that the person is the individualbased on an analysis of the second audio signals. The audio signals maybe analyzed by any of the previously discussed techniques. For example,a voice print of the received second audio signals may be extracted andcompared with the voice prints stored in database 2350 to identify thespeaker. In some embodiments, as described previously, an image of theperson received from apparatus 110 may be compared to the images storedin the database 2350 to identify the speaker. In some embodiments, instep 3516, the processor may store multiple segments of the receivedsecond audio signals corresponding to the individual's speech indatabase 2350 in association with the individual's voice print and imagealong with prior entries (if any). As explained previously, in someembodiments, an entire transcript of the individual's speech may bestored, while in other embodiments, only portions (e.g., keywordsuttered, segments of the conversation, topics discussed, follow-upitems, etc.) of the individual's speech may be stored. In someembodiments, in step 3516, the processor may also retrieve the stored ofthe individual from database 2350 and display the image to the user.

It will be appreciated that the foregoing description may be implementedon devices other than those described, such as any device configured tocapture audio and images in the vicinity of a person. It should also beappreciated that processes and methods discussed above using flow chartsmay be performed in any order. That is, the steps of these flow chartsneed not be performed in the illustrated order. Further, these flowcharts may include any number of additional or alternative steps. And insome embodiments, some of the illustrated steps may be omitted so longas the intended functionality is retained. Further, the illustratedmethods may be incorporated into a more comprehensive process havingadditional functionality not described in detail herein.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, Ultra HD Blu-ray, orother optical drive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. The various programsor program modules can be created using any of the techniques known toone skilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

What is claimed is:
 1. A wearable device for processing audio signals,the wearable device comprising: an image sensor configured to capture aplurality of images from an environment; at least one microphoneconfigured to capture sounds from the environment; and at least oneprocessor programmed to: receive audio signals representative of thesounds captured by the at least one microphone; receive a first imagefrom among the plurality of images captured by the image sensor, thefirst image including a representation of a first individual; using thefirst image, obtain a first audio segment from the audio signals,wherein the first audio segment includes a first portion of the audiosignals in which the first individual is speaking; receive a secondimage from among the plurality of images captured by the image sensor,the second image including a representation of a second individual;using the second image, obtain a second audio segment from the audiosignals, wherein the second audio segment includes a second portion ofthe audio signals in which the second individual is speaking; receive athird image from among the plurality of images captured by the imagesensor, the third image including a representation of the firstindividual; using the third image, obtain a third audio segment from theaudio signals, wherein the third audio segment includes a third portionof the audio signals in which the first individual is speaking; andassociate the first and third audio segments with the first individualand associate the second audio segment with the second individual. 2.The wearable device of claim 1, wherein the at least one processor isfurther programmed to cause the first and third audio segments ortranscripts of the first and third audio segments to be stored inassociation with an identifier of the first individual in a database. 3.The wearable device of claim 2, wherein the at least one processor isfurther programmed to cause the second audio segment or a transcript ofthe second audio segment to be stored in association with an identifierof the second individual in the database.
 4. The wearable device ofclaim 2, wherein the at least one processor is further programmed tocause a time window at which the audio signals corresponding to thefirst and third audio segments were captured by the at least onemicrophone to be stored in association with the identifier of the firstindividual in the database.
 5. The wearable device of claim 4, whereinthe at least one processor is further programmed to cause at least oneof a date or a place at which the audio signals corresponding to thefirst and third audio segments were captured by the at least onemicrophone to be stored in association with the identifier of the firstindividual in the database.
 6. The wearable device of claim 1, whereinthe at least one processor is further programmed to detect that thefirst or second individual is speaking by analyzing the first or secondimage respectively, to identify a facial feature of the first or secondindividual, respectively.
 7. The wearable device of claim 1, wherein theat least one processor is further programmed to: extract a first voiceprint of the first individual from the first audio segment; store thefirst voice print in association with the first image in a database;extract a second voice print of the second individual from the secondaudio segment; and store the second voice print in association with thesecond image in the database.
 8. The wearable device of claim 7, whereinextracting the first or second voice print includes extracting one ormore characteristics of a voice of the first or second individuals,respectively, from the first or second audio segment, respectively. 9.The wearable device of claim 8, wherein the one or more characteristicsinclude at least one of pitch, tone, rate of speech, volume, rhythm,tempo, texture, resonance, center frequency, frequency distribution, orresponsiveness.
 10. The wearable device of claim 7, wherein the at leastone processor is further programmed to: receive additional audio signalsrepresentative of additional sounds captured by the at least onemicrophone; obtain a subsequent audio segment from the additional audiosignals, wherein the subsequent audio segment includes a portion of theadditional audio signals in which a person is speaking; extract a thirdvoice print from the subsequent audio segment; compare the third voiceprint to one or more voice prints stored in the database; and determinethat the person is the first individual when at least one attribute ofthe third voice print matches an attribute of the first voice print. 11.The wearable device of claim 10, wherein the at least one processor isfurther configured to: retrieve the first image from the database; andcause the retrieved first image to be displayed on a display device. 12.The wearable device of claim 10, wherein the at least one processor isfurther configured to store the subsequent audio segment or one or morefeatures extracted from the subsequent audio segment in the database inassociation with the first image and the first voice print.
 13. Thewearable device of claim 10, wherein the at least one processor isfurther programmed to replace the first voice print stored in thedatabase with the third voice print when at least one attribute of thethird voice print is better in quality than at least one attribute ofthe first voice print stored in the database.
 14. The wearable device ofclaim 7, wherein the at least one processor is further programmed to:receive additional audio signals representative of additional soundscaptured by the at least one microphone; receive a subsequent imagecaptured by the image sensor, the subsequent image including arepresentation of a person; obtain a subsequent audio segment from theadditional audio signals, wherein the subsequent audio segment includesa portion of the additional audio signals in which the person isspeaking; compare the subsequent image with one or more images stored inthe database to determine that the person is the first individual; anddetermine that the person is the first individual when therepresentation of the person in the subsequent image matches at leastone aspect of the representation of the first individual in the firstimage stored in the database.
 15. The wearable device of claim 14,wherein the at least one processor is further programmed to replace thefirst image stored in the database with the subsequent image when atleast one attribute of the subsequent image is better in quality thanthe at least one attribute of the first image stored in the database.16. A non-transitory computer-readable medium storing instructions that,when executed by at least one processor, cause the at least oneprocessor to perform a method comprising: receiving audio signalsrepresentative of the sounds captured by the at least one microphone ofa wearable device; receiving a first image from among the plurality ofimages captured by an image sensor of the wearable device, the firstimage including a representation of a first individual; using the firstimage, obtaining a first audio segment from the audio signals, whereinthe first audio segment includes a portion of the audio signals in whichthe first individual is speaking; receiving a second image from amongthe plurality of images captured by the image sensor, the second imageincluding a representation of a second individual; and using the secondimage, obtaining a second audio segment from the audio signals, whereinthe second audio segment includes a portion of the audio signals inwhich the second individual is speaking; receiving a third image fromamong the plurality of images captured by the image sensor, the thirdimage including a representation of the first individual; using thethird image, obtaining a third audio segment from the audio signals,wherein the third audio segment includes a third portion of the audiosignals in which the first individual is speaking; and associating thefirst and third audio segments with the first individual and associatingthe second audio segment with the second individual.
 17. Thenon-transitory computer-readable medium of claim 16, the method furtherincluding: causing the first and third audio segments or transcripts ofthe first and third audio segments to be stored in association with anidentifier of the first individual in a database; and causing the secondaudio segment or transcripts of the second audio segment to be stored inassociation with an identifier of the second individual in the database.18. The non-transitory computer-readable medium of claim 16, the methodfurther including: detecting that the first individual is speaking byanalyzing the first image to identify a facial feature of the firstindividual and detecting that the second individual is speaking byanalyzing the second image to identify a facial feature of the secondindividual.
 19. The non-transitory computer-readable medium of claim 16,the method further including: extracting a first voice print of thefirst individual from the first audio segment; storing the first voiceprint in association with the first image in a database, extracting asecond voice print of the second individual from the second audiosegment; and storing the second voice print in association with thesecond image in the database.
 20. The non-transitory computer-readablemedium of claim 19, the method further including: receiving additionalaudio signals representative of additional sounds captured by the atleast one microphone; obtaining a subsequent audio segment from theadditional audio signals, wherein the subsequent audio segment includesa portion of the additional audio signals in which a person is speaking;extracting a third voice print from the subsequent audio segment; andcompare the third voice print to one or more voice prints stored in thedatabase to determine that the person is the first individual when atleast one attribute of the third voice print matches a correspondingattribute of the first voice print.
 21. A method of processing audiosignals, comprising: receiving audio signals representative of thesounds captured by the at least one microphone of a wearable device;receiving a first image from among the plurality of images captured byan image sensor of the wearable device, the first image including arepresentation of a first individual; using the first image, obtaining afirst audio segment from the audio signals, wherein the first audiosegment includes a portion of the audio signals in which the firstindividual is speaking; receiving a second image from among theplurality of images captured by the image sensor, the second imageincluding a representation of a second individual; using the secondimage, obtaining a second audio segment from the audio signals, whereinthe second audio segment includes a portion of the audio signals inwhich the second individual is speaking; receiving a third image fromamong the plurality of images captured by the image sensor, the thirdimage including a representation of the first individual; using thethird image, obtaining a third audio segment from the audio signals,wherein the third audio segment includes a third portion of the audiosignals in which the first individual is speaking; and associating thefirst and third audio segments with the first individual and associatingthe second audio segment with the second individual.